Search: “reasoning”

SkillRL: Evolving Agents via Recursive Skill-Augmented Reinforcement Learning

arXiv:2602.08234v2 Announce Type: replace Abstract: Large Language Model (LLM) agents have shown stunning results in complex tasks, yet they often operate in isolation, failing to learn from past expe

safetyarxiv-cs-lg

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TAP: Efficient Long-Horizon Agent Pruning via Trajectory-Anchored Recovery

DGX agent

arXiv:2610.09074v1 Announce Type: new Abstract: Emerging long-horizon agentic tasks require repeated model calls, worsening the inference cost of already-costly language models. While narrow agentic t

safetyarxiv-cs-lg

8 Oct 2026

Task-Oriented Key-Layer KV Communication for Efficient Latent Multi-Agent Collaboration

arXiv:2610.08820v1 Announce Type: new Abstract: Large language model-based multi-agent systems improve complex problem solving through collaboration, while latent communication directly transmits mode

agentsarxiv-cs-lg

Applications

Towards Financial World Modeling

arXiv:2610.09048v1 Announce Type: new Abstract: Building a world model requires a state representation useful for planning and decision-making---potentially over tasks unknown at training time. In the

applicationsarxiv-cs-lg

Local Ai

Training Parallel Speculative Draft Models by Directly Minimizing Expected Decoding Rounds

arXiv:2610.10411v1 Announce Type: cross Abstract: Speculative decoding accelerates large language model inference by using a low-cost draft model to propose tokens that the full-size target model veri

local-aiarxiv-cs-cl

VideoEvolve: Co-Evolving Memory and Retrieval for Long Video Understanding

arXiv:2610.10183v1 Announce Type: new Abstract: Long video understanding increasingly relies on external memory to organize massive visual streams into compact representations. However, most memory-ba

agentsarxiv-cs-cv

When Forgetting Looks Like Improvement: Metric Masking in Streaming Diarizer Adaptation and the Price of Rehearsal

model-releasesarxiv-cs-cl

arXiv:2610.08828v1 Announce Type: new Abstract: Small-data adaptation can improve speech detection while degrading speaker attribution. We study this discrepancy in a released streaming diarizer adapt

When Should an In-Context Learner Expand Its Hypothesis Space?

arXiv:2610.09471v1 Announce Type: new Abstract: Learning systems adapt quickly inside a familiar family of models. The harder step comes earlier: deciding, from observations that could be noise, an ex

researcharxiv-cs-lg

Your Prompt Should Do More: Effects of Retrieval Instructions in Embedding Models

arXiv:2610.10508v1 Announce Type: new Abstract: Prompted embedding models have recently received increasing attention, particularly for retrieval, where detailed retrieval instructions are provided as

3D-DefectBench: A Controlled Factorial Study of Vision-Language Model Evaluation Pipelines for Fine-Grained 3D Generation Defects

arXiv:2607.10826v2 Announce Type: replace-cross Abstract: Automated evaluation is essential for scaling generative 3D systems, where exhaustive human review is costly and slow. Yet the reliability of

A doctrine-grounded visual question answering dataset for Tactical Combat Casualty Care

arXiv:2610.07339v1 Announce Type: cross Abstract: Tactical Combat Casualty Care (TC3) requires responders to connect visual observations of injuries and interventions with established clinical guidanc

researcharxiv-cs-ai

Accelerating the Development of PLGA In Situ Forming Depots Through AI-Driven Multi-Objective Optimization

arXiv:2610.08368v1 Announce Type: cross Abstract: Developing long-acting injectable formulations requires the simultaneous optimization of drug loading, release kinetics, viscosity, injectability, sta

researcharxiv-cs-ai

Agreement Is Not Validity: Cross-Model LLM Consensus in Diagnosing Student Failure Modes in K-12 Math Tutoring Dialogue

arXiv:2610.08703v1 Announce Type: new Abstract: In K-12 mathematics tutoring, student-tutor dialogue provides rich evidence of learners' problem-solving processes and sources of difficulty. Learning a

Anchor-driven Multi-modal Multi-scale Expert Selection for Survival Prediction

model-releasesarxiv-cs-cv

arXiv:2610.07694v1 Announce Type: new Abstract: The integrative analysis of histopathological Whole-Slide Images (WSIs) and transcriptomic profiles holds significant promise for cancer survival predic

APEX: Speculate smarter, not deeper

arXiv:2610.07780v1 Announce Type: new Abstract: Speculative decoding reduces large language model inference latency by drafting multiple tokens before target-model verification, but its effectiveness

Ariadne's Thread of LipSync: Unraveling Forgeries via Inconsistency between Lip Motions and Head Poses

arXiv:2610.08417v1 Announce Type: new Abstract: Recent advances in LipSync generation technology have led to the creation of highly realistic videos, posing severe societal risks. However, existing de

researcharxiv-cs-cv

Calibration Is Not Control: Intervention Value for LLM-Agent Oversight

arXiv:2606.21399v2 Announce Type: replace Abstract: Runtime oversight often intervenes when an LLM agent's calibrated failure score crosses a threshold. Yet states with the same failure risk can diffe

ChartBmkAgent: Harness-Governed Multi-Agent Construction of Chart QA Benchmarks from Sparse Error-Taxonomy Specifications

arXiv:2610.08106v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) advance rapidly, while conventional benchmark development lags behind, delaying investigation of newly observed

Closing Ambient Clinical Documentation Gaps with Automated Provider Queries

arXiv:2610.07502v1 Announce Type: new Abstract: Provider queries are clarifying requests sent by clinical documentation specialists to physicians to close gaps in the clinical note and ensure accurate

Continuous Memory Machines

arXiv:2610.07907v1 Announce Type: new Abstract: Recurrent neural networks typically compress information into a single vector-valued recurrent state, forcing short-term computation and long-term reten

researcharxiv-cs-ai

DART-ES: Difficulty-Aware Reweighting and Targeted Replay for Fine-Tuning LLMs with Evolution Strategies

arXiv:2610.06993v1 Announce Type: cross Abstract: Evolution Strategies (ES) enable memory efficient full parameter fine-tuning of large language models (LLMs) using only forward computation. However,

DepthWorld: 3D World Model for Robot Manipulation

arXiv:2610.08780v1 Announce Type: cross Abstract: World models offer a data-driven alternative to traditional simulators for robotics, with applications spanning policy evaluation, improvement, and pl

DIPrune: Task-Aware Token Pruning with Dual Importance for Efficient Multimodal Language Models

model-releasesarxiv-cs-cv

arXiv:2610.08341v1 Announce Type: new Abstract: Recent training-free pruning approaches for Multimodal Large Language Models (MLLMs) effectively cut computational overhead by exploiting visual redunda

DLoop: Looped Speculative Decoding

arXiv:2610.07659v1 Announce Type: new Abstract: Speculative decoding accelerates autoregressive generation in large language models. In each drafting stage, a lightweight draft model proposes tokens t

DSV-Mem: Evaluating Multimodal Memory in Professional Workflows for MLLM Agents

arXiv:2610.08102v1 Announce Type: new Abstract: Conversational MLLM agents are increasingly expected to assist in professional workflows, from AI research and engineering design to product management

Dual-Modality Multi-Stage Adversarial Safety Training: Robustifying Multimodal Web Agents Against Cross-Modal Attacks

arXiv:2603.04364v2 Announce Type: replace-cross Abstract: Multimodal web agents that process both screenshots and accessibility trees are increasingly deployed to interact with web interfaces, yet the

Dynamic Positional Attention Modulation for Parameter-Efficient Fine-Tuning of Large Language Models

arXiv:2610.07848v1 Announce Type: cross Abstract: Parameter-efficient fine-tuning (PEFT) has become a standard approach for adapting large language models to downstream tasks. However, most existing P

Enhancing Diffusion Language Models with Autoregressive Post-Training Weights

model-releasesarxiv-cs-lg

arXiv:2610.08108v1 Announce Type: new Abstract: Diffusion language models (dLLMs) have emerged as a promising alternative to autoregressive (AR) language models, offering flexible token-update orders

EPOCH: Reliable Discovery through Evidence-Governed Search

arXiv:2610.06986v1 Announce Type: new Abstract: AI research agents are increasingly used to search over programs, mathematical constructions, and proofs. However, existing systems typically optimize e

Exact-Solution Volume and Length Generalization in Transformers

arXiv:2610.07676v1 Announce Type: cross Abstract: Research on transformer expressivity shows whether a transformer is capable of solving a given task, but gives little indication of whether the soluti

Harness Engineering for Software Engineering via Modular Executable Dev-Primitives

arXiv:2610.07832v1 Announce Type: cross Abstract: Large language models (LLMs) equipped with terminal access have demonstrated strong capabilities in automating software engineering tasks. However, ex

Hardware

Hybrid Latent Attention for Looped Language Models

arXiv:2610.07940v1 Announce Type: cross Abstract: Looped language models apply the same stack of layers T times to each token, which deepens the model without adding parameters but multiplies its key-

hardwarearxiv-cs-ai

Joint Workflow and Prompt Optimization for User Behavior Simulation

arXiv:2610.07663v1 Announce Type: cross Abstract: User behavior simulation is the computational modeling of user interactions within information systems through the use of simulated agents in place of

agentsarxiv-cs-ai

Language Unalignability: Why Some Concepts Resist Cross-Cultural Benchmark Evaluation

model-releasesarxiv-cs-cl

arXiv:2610.08303v1 Announce Type: new Abstract: Current evaluation of multilingual Large Language Models (LLMs) rests on an implicit Translation-Isomorphism Assumption (TIA): that semantic structures

Large Language Model Orchestration under Heterogeneous Preferences via Explicit Persona Inference

arXiv:2610.07587v1 Announce Type: new Abstract: LLM orchestration investigates how an orchestrator coordinates a group of autonomous agents to achieve common goals or maximize collective welfare. The

agentsarxiv-cs-cl

Learn2Play Bench: How Well Do LLM Agents Learn from Experience in Unfamiliar Environments?

arXiv:2610.08215v1 Announce Type: new Abstract: Learning from experience is essential for LLM agents to adapt to unfamiliar and dynmaic environments. Evaluating this ability is therefore important for

Tutorials

Learning from Failures: A Failure-Driven Prompt Refinement for LLM-Based Vulnerability Analysis

arXiv:2610.08405v1 Announce Type: cross Abstract: Large Language Models have emerged as promising tools for software vulnerability analysis, but their effectiveness depends heavily on prompt design. E

tutorialsarxiv-cs-ai

Learning to Retrieve via Reinforcement Learning in Embedding Space

arXiv:2610.07731v1 Announce Type: cross Abstract: Dense retrieval models are typically trained with contrastive objectives that learn effective representations but do not directly optimize retrieval m

Leveraging a four-quadrant approach for evaluating Redpine Science

model-releasesarxiv-cs-cl

arXiv:2610.07937v1 Announce Type: new Abstract: Redpine Science gives models and agents a single access point to a wide range of peer-reviewed literature, queried directly through the Model Context Pr

LOGIC: An LLM Benchmark for Intent-Grounded Change Impact in Aerospace Electrical Systems

arXiv:2610.07580v1 Announce Type: new Abstract: Aerospace electrical-design revisions can contain multiple genuine changes, although an engineering request may authorize only a subset. Propagating eve

Tutorials

Mask-Guided KV Cache Eviction in Block Diffusion Language Models

arXiv:2610.06996v1 Announce Type: cross Abstract: Block diffusion language models keep a large key-value (KV) cache throughout generation and attend to it at every denoising step, limiting both memory

tutorialsarxiv-cs-ai

Tutorials

Mathematical Proof Assistants for Teaching Logic: The LogiKEy Methodology

arXiv:2610.08214v1 Announce Type: new Abstract: We report on an approach to teaching logic to mixed groups of computer science, mathematics, and philosophy students, based on the logico-pluralistic Lo

tutorialsarxiv-cs-ai

Minimal Witness Reinforcement Learning

arXiv:2610.07226v1 Announce Type: cross Abstract: ``What are the irreducible conditions that are sufficient to produce an outcome?'' is one of the most common questions that recur across computation a

Natural Language Questions as an Interface for Knowledge Graphs: QRAKEN Graph Distillation and Semantic Self-Healing

arXiv:2610.08095v1 Announce Type: new Abstract: Natural-language access to RDF knowledge graphs is a core Semantic Web ambition. Large language models (LLMs) have advanced Text-to-SPARQL, yet on unfam

Nucleus Speculative Decoding: Plausibility-Aware Verification Beyond Exact Distribution

arXiv:2610.07822v1 Announce Type: new Abstract: Speculative decoding accelerates autoregressive generation by using a lightweight draft model to propose multiple tokens that are verified by a target m

On-Policy Distillation with Negative-Policy Rollouts

arXiv:2610.07874v1 Announce Type: new Abstract: On-policy distillation (OPD) has been widely studied as a post-training method in which a student model obtains token-level supervision from a stronger

safetyarxiv-cs-lg

OntoPlan: An Ontology-Grounded Scene Representation and Agentic Framework for Scalable Robot Task Planning

arXiv:2610.07649v1 Announce Type: new Abstract: Large language model (LLM)-based robot task planning is promising for open-ended instruction following, but degrades on long-horizon tasks in large envi

agentsarxiv-cs-ro

Personal-Agent Mediated Recommendation with Cross-Platform User History

arXiv:2610.07588v1 Announce Type: new Abstract: Modern recommendation is shifting from platform-centric personalization toward user-governed personalization, where a personal LLM agent can act on the