New Computation and Language (cs.CL) papers, Thursday 22 October 2026
136 papers announced on arXiv. Get them ranked for you, free →
RESEARCHHUB: A Personalized Literature Discovery System for Biomedical Preprints
AI summaryResearchHub is an interactive system that personalizes biomedical preprint discovery by retrieving relevant papers and encouraging novelty, diversity, and curriculum ordering. Its bi‑encoder retriever, prism, achieves 12.4 % higher Recall@10 than SPECTER2‑Search on a 44k MedRxiv dataset.
TAGR: Task-Adaptive Geo-Semantic Retrieval across Heterogeneous Urban Delivery Tasks
AI summaryTAGR builds an LLM‑derived geo‑semantic prior and task‑specific retrieval banks to share regional knowledge across demand forecasting, ETA prediction, and next‑stop prediction, using a task‑adaptive augmentation module that reduces mismatch and negative transfer. It outperforms baselines on three city datasets.
Beyond source attribution: Address authorship and comparability decisions in an LLM-built scholarly knowledge graph
A knowledge graph needs a stable address for its evidence even where the literature offers competing classifications and agrees on none. Yet an address chosen for retrieval readily passes for the only classification available. This happens whenever records gathered under one…
How Far Can the Gemma 4 Speech Encoder Go Without Changing Its Weights? Saudi and Gulf Arabic Speech Recognition with a Frozen and an Adapter-Augmented Encoder
AI summaryFrozen Gemma 4 encoder with a 22.6 M‑parameter CTC reader achieves similar CER to a 2 B‑parameter Arabic recogniser while running 3.4× faster on CPU. Adding 3.1 M adapters and a hybrid CTC/attention reader matches or outperforms the fine‑tuned model on Saudi and Emirati speech and reaches 139× real time on GPU.
Translation and data ethics
AI summaryExplores ethical challenges in translation data—copyright, ownership, bias, and issues specific to minority language communities and the digital divide—highlighting power imbalances, responsibility, and regulatory responses in translation studies.
Qwen-Audio-3.1-ASR Technical Report
We present Qwen-Audio-3.1-ASR, a family of audio-language models that builds upon its predecesor Qwen-Audio-3.0-ASR with improved speech recognition and expanded capabilities in speaker-diarized transcription and general audio understanding. The family retains support for 30…
A Reverse-Gold Theorem: Positive-Data Identification by Substrate-Bounded Contrastive Learners
Gold's theorem shows that no learner can identify an arbitrary language in a superfinite class from positive presentations alone. We prove a positive result of a different shape. Fix an innate \emph{recursive contrast operator} $R$ mapping any string to a finite set of…
Automating ML for Science: Can Frontier Agents Climb Scientific Hills in the Wild?
AI summaryAgent4Sci tests how well frontier agents improve scientific ML models across 20 benchmarks. Claude Opus gains 10.4% on average, mainly via ML tricks; adding research‑ideation plugins boosts it to 20.1% with more domain‑relevant progress.
SkillEval: Learning Interpretable Ability Profiles of LLMs via Cognitive Diagnosis Models
AI summarySkillEval automatically extracts interpretable skills from benchmark questions and uses cognitive diagnosis models to estimate each LLM’s per‑skill ability, producing calibrated, concise skill profiles that outperform average‑score evaluation in predicting unseen performance and enabling cost‑effective routing and adaptive testing.
Scaling Therapy Supervision: Assessing Psychotherapy Fidelity Using Large Language Models
AI summaryAutomates psychotherapy fidelity assessment with LLMs, generating actionable supervisory feedback from clinical transcripts; evidence-first reasoning yields best fidelity estimates, and the system reliably differentiates CBT from non‑CBT sessions while experts find feedback useful.
Measure First, Then Allocate: Calibrated Rollout Budgets for MCP Agents in Reinforcement Learning
AI summaryThe paper introduces Discord for evaluating harnesses and BRACE for allocating rollout budgets using reward disagreement. BRACE improves policy training efficiency in tool-using language models, outperforming strong baselines with fewer rollouts across multiple benchmarks.
Is Anyone Enforcing the Cartel? What Reinforcement Learning Teaches LLM Agents in Markets
AI summaryLLMs trained by RL collude when enforced, but only if they see a punishment or imitate a rival’s cuts, not through strategic deterrence. An audit shows that only agents that directly match large cuts can enforce a cartel, while others merely follow static rules.
CALLIOPE: Private Insights into AI Use, Without Embeddings
AI summaryCALLIOPE is a top‑down framework that builds topic hierarchies from large unstructured corpora, using LLMs directly instead of embeddings, while providing differential privacy. It outperforms embedding‑based DP methods, especially in high‑privacy settings.
Where Do Legal Factors Live in LLMs?
AI summaryLLMs encode all 26 CATO legal factors in their activations: each factor can be linearly decoded with AUCs 0.86–1.00 across Qwen3‑1.7B, Qwen3‑8B, and Gemma‑2‑2B, and single‑feature sparse readouts locate the factors at specific layer–feature coordinates.
Combining Long-Term Memory and LLMs Comes at a High Cost: From Prompt Injection to Persistent Compromise
The widespread adoption of generative artificial intelligence in recent years has brought a parallel rise in prompt injection and jailbreak attacks. By embedding malicious instructions, these exploits manipulate language models to bypass intended behavior and operational…
LIFT: Scoped Constraints for Evaluating and Training Instruction Following
Common instruction following (IF) benchmarks, such as IFEval, evaluate the ability of language models (LMs) to follow simple constraints that can easily be verified programmatically. These same constraints, in turn, are used as reward signals for reinforcement learning. This…
A Parallel Multi-Dialect Arabic Dependency Treebank
AI summaryMADARTB is a 5,000‑sentence parallel dependency treebank covering five Arabic city dialects plus MSA, annotated with the CATiB scheme. A supervised parser attains 87.5 % LAS, outperforming LLMs that reach 85.7 % LAS across all varieties.
Where Should a Stuck Agent Spend Its Next Tokens? Causal Attribution of Inference-Time Resources
Inference-time scaling is often treated as a token-budget problem, but a failed coding agent can seek qualitatively different resources: computation, information, or feedback. We introduce Banzhaf Resource Attribution (BRA), a controlled factorial framework that measures each…
How to Use Parallel Data for Multilingual Pretraining
AI summaryThe paper proposes concatenating each document with its translation during multilingual pretraining (PPT) and shows that a two‑stage method—PPT followed by standard pretraining—consistently outperforms single‑stage pretraining on language understanding and cross‑lingual tasks within the same token budget.
KIRA: A Full-Stack Reading Screening System with Dual-Pathway Analysis for Children
AI summaryKIRA pairs word‑level fluency scoring with phoneme‑level error detection for early‑grade reading screening, using child‑adapted recognizers that cut word error rates to 8.84% and phoneme error rates to 23%. It provides teachers with fluency scores and phoneme feedback via a web interface.
Beyond Correct Answers: How Compositional Cues Shape Visual Judgments
Visual question answering requires models to assign attributes and relations to the correct objects, a problem known as visual binding. Structured prompts make these assignments explicit through labeled fields. Yet an improvement from the full prompt does not reveal which labels…
To Manipulate or Not: Opinion Dynamics in LLM Interactions
AI summaryLLM‑LLM debates on climate‑change attribution show that opinion shifts depend on the specific models used, not just on assigned stances. Same‑model exchanges narrow views for one model, while cross‑model pairs widen gaps; persuasive prompts do not consistently boost influence.
SANE: Automated Scorer-Specific Metamorphic Testing for LLM Judges
AI summarySANE automatically generates scorer‑specific perturbation recipes for LLM judges, validates them, and produces 3.7× more test families than JRH while detecting deficiencies across domains and aligning judge rankings with human faithfulness judgments (ρ=0.852).
Building AI Research Teams for Autonomous Discovery of Linear Attention Architectures
AI summaryA team of autonomous AI researchers with distinct disciplinary backgrounds, private histories, and adaptive allocation collaboratively discovers linear attention architectures. The system outperforms GDN2 at 1.3B scale across language, reasoning, and retrieval tasks, showing each component—diversity, memory, and allocation—is essential for progress.
CSD: Common-Support Distillation for Cross-Tokenizer Knowledge Transfer
AI summaryCommon-Support Distillation aligns teacher and student vocabularies by building a shared support for each input chunk and matching induced logits with a single objective. It outperforms prior cross-tokenizer methods, boosting student accuracy by up to 4.2 points in several Llama/Gemma setups.
Is Refusal Direction Universal across Low-Resourced Languages?
AI summaryThe paper tests refusal directions in three low‑resource languages (Belarusian, Bashkir, Tajik) on Qwen2.5‑14B-Instruct and finds that transfer is graded, not universal: safe‑aligned languages transfer well, but strongly misaligned ones fail to modulate refusal, weakening safety guarantees.
Guilt by Association: Semantic Leakage Drives Associative Gender Bias
AI summaryThe paper shows that semantic leakage—where unrelated concepts in a prompt bias model outputs—persists even without explicit attribution, and that this leakage similarly drives gender stereotypes when gendered concepts are merely present in the scene.
Scan or Index? large Evidence Coverage, Answer Quality, and Cost in Long-Document RAG
AI summaryThe paper compares 11 retrieval strategies for long‑document RAG, showing that Hybrid + Jev gives the best evidence coverage (94.1%) but Hybrid + Decisions yields the highest answer accuracy (0.892). Full scans increase latency and cost without consistent quality gains.
Language Impairment as a Lens onto Large Language Models
AI summaryThe paper introduces a neuropsychology-inspired interpretability framework that lesions or steers LLMs to create aphasia-like deficits, finding that language functions are localized to discrete circuits yet densely entangled, with steering generalizing but lesioning being asymmetrical.
FlashNext-RL: A System Optimization Study for Long-Horizon Agentic RL of Qwen3.8-Flash-Next
AI summaryFlashNext‑RL is a model‑specific RL training framework for Qwen3.8‑Flash‑Next that freezes early layers, offloads memory, and optimizes mixture‑of‑experts and sparse‑attention kernels. It boosts non‑padding throughput to 4,438 tokens/s/GPU (213 TFLOPs) and enables 131 k‑token context long‑horizon RL in 22 min per iteration.
VeriEvolve: Evaluator-First Evolution for Tool-Using Agents
A central obstacle to self-improving agents is the absence of a reliable fitness signal for realistic stateful tasks. We introduce \textsc{VeriEvolve}, an evaluator-first evolution framework that constructs this missing evaluation layer before optimizing the agent. From task…
Target Regeneration Shrinks the Gains of Draft-Training Objectives in Speculative Decoding
AI summarySpeculative decoding uses a draft model whose tokens are verified by a target model; new draft‑training objectives improve acceptance on externally written responses, but their benefit largely disappears after the target model greedily regenerates the draft. The paper shows this shrinkage across data, verifiers, and decoding settings, concluding that gains should be evaluated against a target‑regenerated baseline.
Our Own Jev: Typed Decisions in One Forward Pass, from an In-House Judge to Open LoRA Models
AI summaryTyped decisions predict option probabilities in one forward pass by reading logits or hidden states, replacing expensive text generation. The paper shows a LoRA‑adapted 35B model reaches 79.3% on JevBench, matching commercial performance, while a 1B system triages 148K coding sessions cost‑effectively.
A Large-Scale Evaluation of Role-Enhanced Zero-Shot Chain-of-Thought Prompting
This paper proposes Role-Enhanced Zero-Shot Chain-of-Thought (RE-ZS-CoT), a prompting framework that systematically integrates domain-specific expert roles to enhance reasoning in large language models (LLMs) without task-specific examples. We assess two versions of our…
Same Answer, Different Reasons: Trace-Level Divergence in Multilingual Large Language Models
AI summaryThe paper develops a trace‑level divergence framework to compare reasoning traces of multilingual LLMs, measuring structural similarity, order consistency, and holistic similarity. It shows that holistic similarity overestimates agreement, with 52–67% of high‑scoring examples lacking structural alignment.
Multi-level Encoder with Global Topic for Task-oriented Dialogue Summarization
AI summaryThe paper presents MLEGT, a multi‑level encoder that learns temporal/spatial dependencies and extracts a global dialogue topic; a topic‑guided pointer then generates accurate task‑oriented summaries, outperforming baselines on Chinese police and medical datasets.
GRAM: Group-wise Rank-Aware Model Merging via Subspace Alignment
AI summaryGRAM merges fine‑tuned models by grouping tasks with aligned subspaces, then performing SVD within each group; this reduces subspace interference. It consistently outperforms prior methods on GLUE, CLIP, and medical LoRA benchmarks.
GRAFT: Restoring the Missing Sampling Interface in Continuous Diffusion Language Models
AI summaryContinuous diffusion language models generate full sequences at once, which collapses the rare‑word tail. GRAFT restores missing word‑choice decisions, improving rare‑word coverage by 34–109 % and aligning overall word‑frequency profiles with human text.
VeeReview: Measuring Build-Verify Pairs in Agent-Authored Pull Requests from a V-Model Perspective
Context: Reviewing a pull request requires verification evidence that addresses the proposed change. Coding agent benchmarks and studies of pull request outcomes do not directly assess this relationship. Objective: We test whether the V-Model's build and verify pairs can measure…
Why is AI-generated language so hard to understand?
AI summaryThe paper measures psycholinguistic difficulty in AI text and finds large-scale language models use more complex structures (long dependencies, low‑frequency n‑grams, inanimate‑subject animate‑verb) than humans or paraphrases. Fine‑tuning with a length penalty shows that higher information density drives this misalignment.
Carving Radiology Reports at Their Evidential Joints: Anatomical Region Sets as Generation Units
AI summaryRadCarve generates radiology reports by carving them into units defined by anatomical region sets extracted from statement-region correspondences. It jointly predicts unit existence and region composition, yielding a 9.4% CXRFEScore boost, 2.4× higher binding F1, and state‑of‑the‑art report results with far fewer parameters.
TeachBack: Learning to Teach via Bidirectional Preference Optimization for On-Policy Self-Distillation
AI summaryTeachBack learns a Teacher that also learns how to teach, using bidirectional preferences to shape prompt length and style. The method improves student performance on math and logic tasks, matching Teacher accuracy while encouraging exploration and reducing reference reliance.
TrainBridge: Agentic NPU Training Optimization Guided by GPU Compilation Evidence
AI summaryTrainBridge is an agentic framework that transfers optimization strategies from mature GPU compilation stacks to improve training efficiency on NPUs. It achieves an average 1.7x speedup across various deep learning workloads by using GPU execution evidence to guide NPU program rewrites.
LTEM: LLM-Friendly Table-Enhanced Markdown for Table Understanding and Generation
AI summaryLTEM adds minimal span and header markers to Markdown, enabling LLMs to parse and generate complex tables with merged cells and multi‑level headers. It matches HTML accuracy while using fewer tokens and yielding far fewer invalid outputs, outperforming LaTeX in generation quality.
Reasoning-Search-Augmented LLMs: An Operational Taxonomy, Benchmark Analysis and Controlled Evidence Synthesis
AI summaryThis paper proposes an operational taxonomy for reasoning-search-augmented LLMs based on four architectural axes and learning signals. It analyzes benchmark demands and synthesizes evidence on the quality-cost tradeoffs of these interactive retrieval systems.
norregel: a preregistered benchmark of dated numeric values in Norwegian law
AI summarynorregel evaluates how well Norwegian language models retrieve dated numeric values from statutes. It finds that models correctly answer about 45–40% of items, and shows a significant increase in outdated answers for post‑cutoff values, though results vary across models.
Understanding Target Probability Reuse in Speculative Decoding
Speculative decoding uses a target model to verify a tree of draft tokens in parallel. We study whether the resulting next-token distributions can help select the next tree. For unrestricted target models, we show that guaranteed exact reuse requires a distribution computed at…
Preference Is Not Permission: Personalizing the Auto-Purchase Line in Agentic Commerce
AI summaryThe paper defines a personalized auto‑purchase line with a three‑state decision (don’t buy, confirm, buy) based on warrant, reversibility, price, and urgency, and shows that shallow models trained on a few dozen personal labels can predict this boundary with 0.79 accuracy, outperforming all prompt‑model baselines.
C$^2$U in RAG: Compositional Concept Unlearning in Retrieval-Augmented Generation
AI summaryC^2U suppresses unwanted concepts in retrieval-augmented generation by adding a bounded delta to query embeddings via an atom bank. It reduces target Hit@5 by 4.25 pp without harming control or exposure, outperforming baseline directions on multiple datasets.
Beyond Binary Labels: Diagnostic Evaluation of LLM Safety with Prompt-Level Rubrics
AI summaryDiagSafe introduces prompt-level rubrics for LLM safety evaluation, replacing binary labels with a 84-label safety taxonomy that scores safety and usefulness independently. It achieves 95.34% agreement with humans and 0.864 κ, revealing model-specific vulnerabilities, especially in Chinese and multi-turn contexts.
MOSAIC: An Omni-Modal Benchmark for Long-Term Memory and Cross-Modal Reasoning in Conversational Agents
AI summaryMOSAIC evaluates omni‑modal conversational agents on long‑term memory across text, image, video, and audio by 923 targeted questions. It diagnoses failures in representation, retrieval, delivery, and reading, revealing modality‑dependent losses and weak refusal behavior.
Let Agents Design How They Search: Automatic Interface Optimization for Agentic Search
AI summarySearchWeaver automatically optimizes search interfaces for language‑model agents by revising corpus artifacts, tools, and instructions based on execution trajectories and rewards. The method raises BrowseComp‑Plus accuracy from 71.6 % to 78.4 % and nDCG@10 from 45.7 to 55.0.
Intergroup Bias Predicts Expressions of Social Judgment and Empathy in the r/AmITheAsshole Reddit Community
AI summaryThe study analyzes 126k comments on 36k r/AmITheAsshole posts, finding that commenters are more likely to judge the poster as not the asshole and display greater empathy when the poster shares their gender or age; older commenters and nuanced group dynamics also shape these judgments.
Hierarchical Masked Diffusion Language Models
AI summaryHierarchical Masked Diffusion Language Models (HMDLM) add a hierarchy level per token, using separate noise schedules to prioritize high‑priority positions during parallel generation. HMDLM cuts perplexity by over 1.5 points on LM1B, 4.4 on WikiHow, and boosts Sudoku solution accuracy by 8.6% versus prior diffusion baselines.
Heard But Not Said: Prompting Understates Temporal Information of Musical Moments in Frozen Large Audio-Language Models
Large audio-language models (LALMs) understand music, yet struggle to locate a described moment in time. We ask whether this reflects missing temporal information or a bottleneck in how the time span is extracted. Using a paired-query probe built from Slakh2100 stem activations,…
Computer Vision and Knowledge-Grounded Language Models for Intelligent Beekeeper Assistance
AI summaryWe build an edge‑deployable pipeline that detects comb‑cell classes from hive photos, converts detections to a structured fact schema, generates constrained factual text, and uses a knowledge base to provide actionable beekeeper recommendations. We release FAIRHiveFrames‑1K and FAIRHiveText‑1K datasets.
Credit Where Credit Is Due: Contrastive Attribution Topology Search for Multi-Agent Systems
AI summaryCATS searches a supergraph of specialized agents, uses contrastive attribution to assign credit to agents, then prunes weak edges and agents, yielding a compact, high‑accuracy team. It outperforms score‑only search by 5.3 pp on BigCodeBench and reduces tokens by 54 %.
Same Probabilities, Different Advice: Gain-Loss Framing Effects in High-Stakes LLM Decision Support
AI summaryThe paper introduces FrameBench‑HPS, a 2,880‑prompt benchmark for high‑stakes LLM advice, and finds that gain‑loss framing swaps recommendations in 32.4% of cases, largely altering uncertainty assessments; canonicalizing probabilities reduces this sensitivity.
ExpEvolve: Structuring and Reusing Experience for Open-Ended Discovery
AI summaryExpEvolve injects idea-level extraction into evolutionary coding agents, identifying high‑level factors that explain program performance and using them for guided exploitation and exploration. Across 11 math tasks it boosts final performance and sample efficiency by up to 5×, with transfer to other LLMs and MLS‑Bench.
LLM-Based Exogenous World Modeling for Dynamic Scheduling
AI summaryAn LLM predicts only uncertain future job arrivals in a structured world model for dynamic scheduling. The resulting Afterstate World-Model Planning algorithm reduces weighted tardiness by 6–46% compared to the best classical dispatching rules across multiple benchmarks.
Asking What a Director Sees Can Change What Vision-Language Models Choose
Vision-language models are now routinely tested on theory of mind: whether they can work out what another person sees, knows, and means. A common design asks the model to report that perspective and then to act for the person, and reads a correct report beside a wrong action as…
OSynth: A Longitudinal, Context-Sensitive Evaluation of Meeting Understanding
Meeting notetakers are increasingly deployed in settings where longitudinal context is available including calendar metadata, prior meetings, emails, slides, and speaker enrollments; however, meeting corpora remain limited in terms of size, domain coverage, and available…
Signals and Queries: Token-Efficient Agentic Context Discovery
User-facing LLM agents must identify which behavioral policies apply at each turn, but evaluating every policy with an LLM is costly. We introduce Signals and Queries (S&Q), a lightweight first-stage relevance filter developed for Parlant 4.0 that removes at least 30% of…
OACap: Caption-Guided Active Observation for Detailed Video Captioning
AI summaryOACap is a caption-focused agent that requests targeted observations to fill missing info, then revises captions iteratively. It improves video‑captioning metrics and introduces FSCap‑Bench with CSFS and CSRS scores to evaluate factual and semantic fidelity.
Seeing the Forest for the Trees: Emergent Agreement from Individual Conversations
AI summaryThe paper shows that while individual LLM conversations’ internal agreement signals fluctuate and poorly track external agreement, aggregating these signals across many conversations reveals a simple Markovian structure linking internal and external agreement, with external agreement predicting the next turn.
Policy Central: Detecting Conflicts, Redundancy, and Similarity in Deployed Police Policy
Policy Central is a deployed system that helps police departments find relationships between sections of their written policy: whether two sections conflict, are redundant, are merely similar, or are different. We describe the system and compare two architectures.…
ChronoSynth: Constraint-Aware Question Synthesis over Temporal Knowledge Graphs
Knowledge graph question generation (KGQG) generates natural-language questions from structured graph evidence and a target answer. When the input graph is temporal, a valid question must also preserve the answer field, time condition, reference event, and temporal order that…
Reader--Writer Coupling in Transformer MLPs
A transformer MLP gives each hidden channel two roles: a reader that decides when the channel responds, and a writer that decides what it adds back to the residual stream. Read as a key--value memory, the layer can be measured one factor set at a time: the pairing of readers to…
Uncensored Is Not Unbiased: Abliteration Removes Refusals but Not Script-Dependent Bias About Taiwan
"Abliterated" ("uncensored") builds of open-weight language models remove the activation direction that mediates refusal, and are popular partly on the assumption that a model that no longer refuses is also free of political censorship. We test this on two matched pairs of 2-bit…
Decodability Is Not Controllability: Local and Anisotropic Speaker Control in Continuous-Latent TTS
High linear decodability does not entail causal controllability in continuous-latent text-to-speech (TTS). In VoxCPM2, position-matched probes and equal-norm interventions across 30 speakers and 60 directed dyads expose this separation: reference-audio states at the final…
Data Schedules and Learning Rate Schedules Should Be Designed Together
Mid-training (MT) is a training phase in LLM training pipelines that continues pretraining (PT) on smaller datasets of higher value. Taking the PT and MT phases as a whole, training usually starts with only PT data and then transitions completely to MT data. In this work, we…
B-CARE: Blind Classwise Assessment of Relative Evidence for Stance Error Ranking
Stance classifiers make errors that can be costly to identify under limited human-review budgets. Prioritizing potentially incorrect predictions remains challenging, particularly across heterogeneous base models. We introduce B-CARE (Blind Classwise Assessment of Relative…
Closed-Loop Dual-Reference Guidance for Accent Control in Cross-Lingual Voice Cloning
Zero-shot cross-lingual voice cloning often carries the reference speaker's accent into the target language. We propose closed-loop dual-reference guidance, which pairs the original reference with a reusable native anchor constructed by voice conversion (VC). The references…
Don't Stop Long-Context Continual Pre-Training Too Early: An Empirical Scaling Law
Long-context continual pre-training (LCCP) is widely used to improve long-context capability, but how its benefits scale with the number of tokens used for LCCP remains unclear. We investigate this question through modeling, monitoring, and validating the benefits of scaling…
Memory-Centric Continual Learning for AI Agents
Autonomous agents powered by foundation models can reason, plan, and use tools, but their fixed weights and context limits hinder sustained learning from experience over long-term deployment. While external memory systems attempt to bridge this gap, they mostly act as passive…
Right On Q: Recovering Reasoning under Quantization via Self-Generated Trajectories
Quantization-aware training (QAT) aims to recover reasoning capabilities in low-bit large language models (LLMs). Existing recipes typically supervise the quantized student with the original full-precision model on fixed response sequences. However, the resulting low-bit…
ECHO: Emergent Common-Mode Hidden Offsets Drive Vocabulary Collapse in Preference Optimization
AI summaryPreference optimization can silently accumulate a shared hidden state shift that blinds standard ranking metrics while driving text generation collapse. Masking this common-mode offset from the loss successfully prevents the failure.
Beyond Small Talk: Modeling Disclosure Trajectories in Human-AI Conversation
AI summaryTrajectory‑based Disclosure Evaluation (TraDE) measures how prior disclosure history influences AI responses to the same current disclosure level. Across models, TraDE reveals that AI often accepts or challenges user framing differently depending on the previous trajectory, diverging most when users retract disclosure.
CCG Diffusion: Combinatory Categorial Grammar-Driven Compositional and Syntactically Disambiguated Text-to-Image Generation
AI summaryCCG Diffusion uses Combinatory Categorial Grammar to read syntactic relations directly from a parse, then rewrites prompts and guides attention in diffusion models. It achieves top performance on attribute binding, spatial relations, and prepositional phrase disambiguation for Stable Diffusion and Sana.
A KAN-based Non-linear Factorization for Embedding Layer Compression
AI summaryWe replace the linear bottleneck of low‑rank embedding compression with learnable KAN‑style univariate projections, preserving representational capacity. On Word2Vec, the KAN‑compressed layer outperforms a standard low‑rank baseline at the same parameter count.
BIRD-RL: Scaling Agentic Reinforcement Learning over Stateful Data-Centric Environments
AI summaryBIRD‑RL introduces a four‑stage curriculum and a trajectory‑scoped execution infrastructure that allow large language models to learn data‑centric tasks like SQL debugging without isolated databases, achieving a 12.8× spatial efficiency gain and 44–48% success rates on the BIRD‑CRITIC‑SQLite benchmark.
DynaWeave: Learning to Adapt through Dynamic Scenario Synthesis
Large language model (LLM) agents increasingly use tools to complete tasks in stateful environments. However, many training environments remain largely static apart from changes caused by the agent's own actions, whereas external events introduced by other users, services, or…
Omnilingual-MsASR: Benchmarking Multimodal, Multilingual, Long-Form Multi-Speaker ASR of Omni Large Language Models
Omni-modal large language models (LLMs) have achieved remarkable progress in audio and video understanding. However, existing models struggle with long-form, multi-speaker videos, especially in understanding the entire video and recognizing who says what and when. To…
Harness Scaling for Generalizable Agentic Reinforcement Learning
Agent harnesses have become a central component of modern agentic systems, shaping how models use tools, manage context, and coordinate long-horizon interaction. Realizing the benefits of these harnesses depends on how effectively the underlying model can make use of them,…
OmniSimulator: Aligning Small Language Models for Authentic Heterogeneous Behavior Modeling
AI summaryOmniSimulator trains small language models with a structured, behavior-oriented reasoning schema and teacher-generated traces, using 9,881 curated user‑interaction examples. It boosts Qwen3‑8B’s simulation accuracy by 34.33%, matching Claude‑Sonnet‑4.5 while enabling self‑hosted deployment.
TenetBench: A Clause-Anchored Russian Corpus for Evaluating Language Models against Professional Codes of Ethics
Codes of professional ethics guide and restrict specialists, who increasingly work with assistants built on large language models. Such an assistant can breach the user's code, or help the user breach it, without the user noticing. Evaluations reporting broad categories of harm…
A Strong MoE Is All You Need for Process Supervision
Process reward models (PRMs) provide fine-grained supervision for multi-step reasoning, yet obtaining reliable process labels remains a central bottleneck. Existing approaches typically rely on human annotation, rollout-based estimation, or auxiliary verification. In this work,…
Keeping Time vs. Understanding Time: Neither Alone Explains Temporal Reasoning in LLMs
AI summaryThis paper tests the assumption that temporal reasoning splits into arithmetic and semantic modes. It shows finetuning transfers poorly within modes, and that benchmark scores are driven by factual knowledge rather than by true arithmetic or semantic reasoning.
Mitigating Query Shortcuts in Chain-of-Thought with Context-Aware Decoding
While Chain-of-Thought (CoT) prompting improves the reasoning performance of large language models (LLMs), recent studies have shown that final answers do not always depend on the generated rationales. In particular, LLMs may rely on query-specific shortcuts rather than the…
Beyond the Vote: When Do LLMs Add Value to Retrieval-Based Tabular Classification?
Retrieval-based in-context learning (ICL) enables large language models (LLMs) to predict tabular labels from similar examples, but it remains unclear whether LLM inference improves on direct voting over those same examples. We address this question by comparing four…
pplx-embed-v2-late Technical Report
In this report, we introduce pplx-embed-v2-late, a pair of 0.6B and 9B multilingual and multimodal late-interaction models. Both models achieve state-of-the-art results within their respective size classes across a range of text, image, and visual document retrieval benchmarks.…
RCSD: Reinforcement and Continual Learning for Domain-Specific Speculative Decoding
Speculative decoding accelerates large language model inference by employing a compact draft model to propose candidate tokens that are verified in parallel by a target model. Conventional training relies on soft-label distillation, which forces the draft model to mimic the…
PACE: Periodic and Channel-Aligned Encoding of Four-Bit Recurrent States
AI summaryPACE is a post‑hoc four‑bit state quantizer that rounds the recurrent state only every few tokens, shares scales per column, and keeps long‑error heads at eight bits. It matches or exceeds eight‑bit baselines on 4–35B models while using ~⅔ the memory.
WRDO: Weighted Refusal Direction Optimization in Aligned Language Models
Aligned large language models are trained to comply with benign requests while refusing harmful ones. However, recent work shows that this refusal behavior is fragile and can often be disrupted by simple activation- or weight-space interventions. In particular, refusal has been…
A Diagnostic Study of Faithfulness in Diffusion Language Models for Table-to-Text Generation
AI summaryDiffusion language models are evaluated for faithfulness in table-to-text generation using ToTTo. While QLoRA‑adapted LLaDA achieves the highest BLEU (24.15), its grounding remains weak; autoregressive models drift from the table, indicating that reference similarity alone fails to capture faithfulness.
Overview of CHIP 2026 Shared Task 1: Patient-Specific Phenotype Concept Recognition in Full-Text Biomedical Literature
Automatic phenotype information extraction plays an important role in rare disease diagnosis, genotype-phenotype analysis, and precision medicine. However, accurately identifying phenotype concepts and determining their associations with specific patients remain challenging,…
LLMs as survey respondents: Comparison to simple semantic baselines
Large language models (LLMs) have been explored as surrogates for human survey respondents. A more promising application is data augmentation, where LLMs predict new responses from partial human data. However, the semantic similarity between questionnaire items is a known source…
Effects of Model Scale and Quantization on Social Bias and Abstention in Question Answering
The deployment of small and quantized language models in resource-constrained settings raises an important fairness question: when a compressed model produces fewer biased responses, does this reflect fairer behavior or greater abstention? We study this problem in Question…
SPARE: Sparsity Allocation via Routing Entropy for Efficient MoEs
Mixture-of-Experts (MoE) architectures scale model capacity efficiently, yet their substantial memory footprint poses a major barrier to deployment. Expert pruning reduces this footprint, but most methods focus on which experts to remove and prune every layer equally. We propose…
Conflicted Records: Typed Adversarial Contracts for Auditable LLM Output Quality Enforcement
LLM-generated records increasingly populate production information systems where they are treated as data assets subject to the same quality expectations as records from operational databases. A single such record can simultaneously satisfy and violate a quality rule: a grounded…
Uni-Mem: Learning a Long-Horizon Agentic Memory Manager with Unified Online Policy Optimization
AI summaryUni‑Mem learns a policy that simultaneously constructs and retrieves long‑term agent memory via end‑to‑end online RL. Stage‑Decoupled GRPO and Event‑Wise Credit Assignment give stage‑specific learning signals, achieving state‑of‑the‑art performance on LoCoMo and LongMemEval.
Scaling Multilingual Pretraining of Pixel Language Models
Pixel language models process text as rendered images instead of a finite vocabulary, which gives them strong cross-lingual transfer abilities. However, not much is known about how these abilities are influenced by larger-scale pretraining. In this paper, we train a family of…
Retrieval Augmented Generation Compositionality
Retrieval Augmented Generation (RAG) grounds large language models in external knowledge. Modern pipelines combine embedding, chunking, retrieval, reranking, query augmentation, context compression, and agentic verification, yet these techniques are typically evaluated one at a…
Accept or Repair: Preserving Passage-Supported Answers in Contrastive Decoding
Large language models often answer from knowledge learned during training, even when a provided passage contradicts that knowledge. To address this problem, contrastive decoding contrasts the predictions under the full and a modified prompt at every token, but it also alters…
Bridging Foundational and Agentic Capabilities of LLMs
AI summaryThe paper introduces an operation‑level framework linking LLM foundational benchmarks to real‑world agent performance, showing that foundational scores only partially predict agentic outcomes. Experiments on fifteen services reveal consistent performance gaps that operation profiles can identify and predict more accurately.
ALIAS: Evaluating Performance and Memorization in LLM-Written TLA+ Specifications
AI summaryThe paper evaluates LLM performance on TLA+ specifications using consistent identifier renaming to detect memorization. It finds that renaming reduces scores modestly, largely due to name‑based grading, and shows that fine‑tuned models lose about half their training advantage when identifiers change.
Evolving with Open Worlds: Towards Recursive Self-Improvement for Future Forecasting
Most contemporary artificial intelligence (AI) systems are released with frozen parameters and improve through external updates, whereas an ideal system would keep improving from its own experience, a capacity we call recursive self-improvement (RSI). Yet studying RSI requires…
Where Do LLMs Learn to Recommend AI?
AI summaryLarge language models already recommend AI topics in about 27 % of responses, rising to 59 % after instruction tuning. Fine‑tuning and prompt phrasing can raise or lower this rate, showing the tendency originates in pretraining and is modulated by training data and query style.
MAPS: Macro-Adaptive Parallel Reasoning for Regulating Search Momentum in Retrieval-Augmented Generation
Retrieval-Augmented Generation (RAG) improves the performance of Large Language Models (LLMs) by retrieving external knowledge. Recently, some RAG methods interleave reasoning and retrieval, enabling LLMs to progressively generate sub-queries and retrieve external knowledge.…
Real PMIDs, Wrong Papers: Harness Design for Biomedical Claim-Verification Agents
Literature-searching language agents are becoming a standard component of scientific assistants for the life sciences, yet there is little controlled evidence on which parts of their harness matter. We run a controlled ablation on biomedical claim verification (SciFact) with the…
MemoryTax: The Bitter Lesson of Agent Memory
AI summaryMemoryTax tests different memory schemes on a 419‑turn LoCoMo conversation, finding that designed memory (Mem0, Zep) only benefits weak models and can be more costly than full context; a strong coding agent (Pi on Claude Opus 5.5) achieves 99% accuracy at <50% of full‑context cost.
S-EDL: Eliciting Self-Evidence from Sequence Likelihoods for Semantic Calibration of LLMs
Calibrating large language models (LLMs) in open-ended generation is uniquely challenging because many distinct token sequences can express the same underlying meaning. Prior calibration techniques target either fixed-set classification confidence or the binary correctness of…
Every Part Has Its Role: Part-of-Speech Effects in LLM Post-Training
Large language models are typically trained with next-token prediction (NTP), which rewards predicting specific tokens rather than explicitly accounting for the semantic similarities between different words or expressions. Prior work has addressed this limitation by augmenting…
Investigating the Impact of Learner Errors on Social Bias in Large Language Models
AI summaryThe paper injects common learner errors into bias benchmarks and finds that grammatical mistakes amplify bias, especially preposition errors, with effects growing as error rate rises. Fine‑tuning on erroneous texts mitigates this increase, showing well‑formed prompts under‑represent real‑world bias.
Benchmarking Large Language Models for Detecting Satirical Code-Mixed Sentences
Code-switching is a natural linguistic phenomenon, yet its impact on high-level reasoning tasks remains largely unexplored in under-resourced languages like Romanian. Natural language processing tasks, such as satire detection, rely heavily on nuanced cultural and syntactic cues…
Unintended Side Effects of Value Training in LLMs
AI summaryValue‑training LLMs on Hofstede dimensions produces spill‑over effects on unrelated values, risk attitudes, social cognition, and safety. The study shows that post‑training alignment needs broader behavioral checks, suggesting careful placement of value fine‑tuning in the pipeline.
Task-Preserving Multilingual Instruction Tuning for Open-Ended QA in WMT 2026 MIST
AI summaryTask-preserving multilingual instruction tuning of Qwen3-8B via LoRA and CrossEuroAlpaca yields a 7.8% relative Gemma‑4 score gain on target-language open‑ended QA and 41.5% macro‑ROUGE‑L boost across ten languages, though non‑target tasks suffer minor drops.
Your Benchmark Is an Empirical Measure Over Difficulty
AI summaryThe paper treats a benchmark as an empirical difficulty distribution, revealing two failure modes: measure mismatch and coverage gaps. It introduces diagnostic metrics and the BenchFill algorithm that rewrites items to reduce coverage gaps, showing many existing benchmarks overweight or miss difficulty regions.
Graph Topology Meets Energy-Driven Pathfinding: Preserving Contextual Consistency in Retrieval-Augmented Generation
Retrieval-augmented generation (RAG) frequently integrates knowledge graphs (KGs) for complex multi-hop question answering. However, their performance is increasingly bottlenecked by the knowledge structuring and reasoning processes. (i) Severe semantic loss: Distilling…
ToM2Vec: Continuous Theory-of-Mind Tokens for Multi-Agent Coordination
Forming an accurate theory-of-mind (ToM) is key to successful multi-agent collaboration and requires reasoning under uncertainty to capture multiple possible partner states. This is hard to do in the text representation space native to large language model (LLM) agents, which…
Towards English - Mizo Legal Machine Translation: Corpus Creation, Analysis, and Benchmarks
AI summaryThe paper releases the first English‑Mizo legal translation corpus of 13,168 sentence pairs from Indian courts, analyses borrowing patterns, and fine‑tunes NLLB‑200 achieving 27.47 BLEU—a 7.16‑point gain—yet still over‑uses English relative to human translation.
Commit or Abstain? System One Decision Models, LLMs, and Missing Legal Authority
AI summarySystem‑One decision models use LLMs to output calibrated probabilities instead of text, allowing them to abstain when no legal holding applies. In CaseHOLD, a decision model matches the best generative LLM (≈88% accuracy) while cutting cost 55× and reducing wrong citations.
CulturalRiddles: A Multicultural Benchmark of Riddles
As LLMs expand their language coverage, evaluating how well they understand and represent diverse cultures becomes increasingly important. However, many benchmarks remain English-centric, with limited cultural coverage and a focus on factual knowledge over its application in…
Remembering Through Change: Provenance-Preserving Hypergraph Memory for Multimodal Agents
Long-term memory for multimodal agents remains challenging, as it must preserve fine-grained visual evidence while tracking entity evolution across sessions. Existing abstraction-based memories compress images into text, often discarding visually discriminative details, whereas…
HiddenLens: A Multi-Layer LLM Framework for Detecting and Resolving Hidden Assumptions in Software Requirements
Natural-language software requirements often omit assumptions about performance, operating conditions, user behaviour, and domain constraints, affecting architecture, testing, and deployment. If undiscovered until implementation, resolving them is far more costly than addressing…
Building Auditable AI Text Detectors with Sparse Autoencoders
AI-generated text detectors are increasingly deployed in consequential settings---peer review, education, admissions, journalism, and content moderation---where their outputs directly affect individuals. Yet the strongest detectors are fine-tuned neural classifiers whose…
Agent Behavior Maps: Locating Agents by What They Would Do, Without Running Them
AI summaryAgent Behavior Maps encode LLM-harness combinations via centered action log‑likelihoods on shared reference trajectories, enabling performance prediction without new rollouts. Across 100 LLMs and four environments, map distances rank‑order trajectory KL, revealing behavioral similarities that persist after controlling for success rates and model size.
What the Author Wrote, What the Agent Reads: Silent Cue Loss in Enterprise Document Ingestion
Enterprise agents act on documents. Before an agent can follow a policy, answer a compliance question or file a ticket, some ingestion layer must turn a Word file or PDF into tokens - and that layer is where an agent deployed in the wild silently loses information. We show that…
Looped Uniform Diffusion Language Models
Uniform diffusion language models (UDLMs) generate text by iteratively denoising sequences of ordinary vocabulary tokens, any of which may be corrupted and subsequently revised. During training, UDLMs must denoise from a context that may itself be unreliable, whereas masked…
GoiBERT: Compact Vietnamese Language Models via Two-Stage Knowledge Distillation
Vietnamese pre-trained encoders have improved rapidly, but the strongest ones are large, multilingual-vocabulary models or depend on external word segmentation, which makes them costly to deploy. We present GoiBERT, a family of two compact Vietnamese encoders with 26M and 49M…
VX-One: Typed, Calibrated Decision Heads for Real-Time Perimeter Triage - An empirical study of unknown-attack honesty, cascade gray zones, and calibration under drift
Perimeter triage faces a latency/semantics trade-off: signature engines are fast and format-safe but blind to unseen attacks, while generative models are semantic but slow and produce unparseable output. In late 2026 a class of commercial System One decision models (TypeSafe…
Did the Problem Actually Get Harder? Testing LLMs on Paired P and NP-Complete Problems Hardened by Adversarial Multi-Agent Rewriting
Many NP-complete (NPC) problems have a polynomial-time (P) twin that differs in a single structural detail, such as 3- versus 2-colouring a graph. A model that reasons rather than recalls should notice which side of this boundary a problem falls on, yet existing benchmarks…
Quant-Arena: Benchmarking LLM Agents for Quantitative Trading Research
Quantitative research is an iterative loop: a researcher proposes a market hypothesis, implements it as an executable signal, tests it on historical data, and decides what to keep. Existing benchmarks score formulas, code, or individual trading decisions, leaving open whether…
Environment Duels: A Microcosm of LLM Co-Evolution
Through a tangle of real-world interactions, from synthetic training pipelines to data shared on the open web, frontier LLMs absorb what their predecessors and competitors create. How well does each model play its role in this ecosystem, as a creator and as a solver? We…
Think Harder Where It Matters: Item-Level Adaptive Recursion for Sequential Recommendation
Existing sequential recommenders usually allocate a fixed computational budget to every item in a user's interaction history, regardless of the item's contribution to the final prediction. We challenge this de facto design with two diagnostic findings. (1) Non-uniform…
LongZero: Enhancing Long-Context Reasoning in LLMs from Zero Data
Reinforcement learning (RL) is an effective way to strengthen the long-context reasoning of large language models (LLMs), but it needs training data that are long, diverse, challenging, and verifiable. Human annotation of such data is expensive, naturally occurring data are too…