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Newest papers, page 199
Follow the Entities: A Corpus Map for Agentic Search
Answering questions and completing tasks over large document collections often requires connecting evidence spread across multiple documents, such as a project's approval recorded in one, its…Asking for What Was Never Requested: Horizontal and Vertical Proactivity in Agents
An agent that uses tools typically responds to what the user explicitly asks, yet completing the task may require information the user never requested.Hidden Reasoning Must Leak, but Need Not Be Readable: Fundamental Opportunities and Limits for Chain-of-Thought Monitoring
Can reasoning models trick chain of thought (CoT) monitors and perform hidden computation without revealing it in their thinking traces?Solving Without Stopping: On-Policy Distillation at Small Scale
On-policy distillation, where a student learns from a stronger teacher's feedback on its own outputs, is a common way to pass reasoning to smaller models.Port-Hamiltonian Latent Deliberation: Mitigating the Deliberation Drift Cliff in Test-Time Compute Scaling
Test-time compute scaling has emerged as a cornerstone of advanced machine reasoning, yet performing iterative deliberation directly within continuous latent representation spaces reveals a…SemOPT: Fixing Semantic Errors in LLM-based Optimization Modeling via Reward-Guided Search
Operations research supports decision-making in domains such as energy, economics, and healthcare.Compiling Learning Problems into Adaptation Programs for Language Models
Model adaptation is typically governed by a fixed recipe, even though different update programs can produce substantially different behavioral outcomes.Look What You Made Us Cluster: Hate Narrative Extraction from Reddit Discourse
Narrative extraction allows us to identify online hate narratives, supporting the construction of rigorous detection systems.Learning to Retrieve Missing Evidence for Long-Term Memory QA
Long-term memory enables language models to use past interactions in future conversations.From Dissonance to Orchestration: Teacher Intervention in On-Policy Distillation
On-policy distillation (OPD) trains a student on its own reasoning trajectories using feedback from a stronger teacher.Hierarchical Compression of Vision-Language Model Benchmarks
Thorough evaluation of vision-language models (VLMs) has become prohibitively expensive, as benchmarks span an ever-broader spectrum of capabilities and new models arrive at a relentless pace.E-MoE: Enhanced Mixture-of-Experts for Non-Factorized Diffusion Language Models
Masked diffusion models (MDMs) generate sequences by progressively unmasking several tokens per denoising step, but their reverse process is typically factorized over positions, limiting sample…RunyaNER: Auxiliary Language Selection for Runyankore NER
Cross-lingual zero-shot transfer and multilingual fine-tuning are promising approaches for NLP tasks such as Named Entity Recognition (NER) in low-resource languages, but in the absence of target…Orthogonal Yet Coupled: Decoupling Geometric Components for Model Merging
Merging pretrained models has emerged as an effective approach for consolidating diverse capabilities into a single unified model.Devils in Question Relay: Source-Conditioned Relay Steering to Mitigate Hallucinations in Audio-visual Large Language Models
Audio-visual large language models (AVLLMs) have made remarkable progress in multimodal understanding and reasoning through interactions among visual, auditory, and linguistic information.MERGE: Multi-LLM Ensemble for Retrieval via Generative Enrichment
Large Language Models (LLMs) are increasingly used to enrich user queries in information retrieval (IR) so that a standard retriever such as BM25 can bridge vocabulary gaps with the target corpus.Pair Difficulty Matters: Rethinking Pairwise LLM-as-a-Judge Evaluation and Consistency
Large Language Model judges are widely used to rank texts and text-generating systems through pairwise comparison, and their reliability is typically assessed via three proxies: position bias…Rational Clarification by Assistive Agents via Value-of-Information Reasoning
Users of language-based assistive agents often make ambiguous requests.FOCUS: Training-Free Decision-Preserving Context Compression for LLM Agents
LLM agents accumulate interaction histories that grow linearly with task length, causing quadratic inference cost scaling and performance degradation from attention dilution.Authority Bias in Language Models: Source Deference and User Agreement Are Not Interchangeable
Language models tend to agree with whatever a user asserts, and post-training increasingly targets this sycophancy so that models evaluate claims on their merits rather than deferring to the user.