Search: “llm”

ProgAgent: Learning Tool Orchestration through Programmatic Training

arXiv:2610.14774v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly expected to orchestrate diverse tools over long-horizon tasks. Existing agents typically use Direct Tool C

agentsarxiv-cs-ai

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PXRD-Bench: How Close Are We to Determining Crystal Structures from Powder Diffraction?

DGX agent

arXiv:2610.16480v1 Announce Type: new Abstract: Determining crystal structures from powder X-ray diffraction (PXRD) would enable routine characterization of newly synthesized materials, including thos

model-releasesarxiv-cs-ai

15 Oct 2026

Qwen-MT-Lumen at WMT2026: Instruction-Specialized Translation via Quality-Aware Training and On-Policy Distillation

arXiv:2610.19758v1 Announce Type: new Abstract: We present Lumen, The open-source model is available at, our submission to the WMT 2026 General Machine Translation Shared Task. We focus on instruction

RASSG: Repair-Aware Semantic Survival Gating for Streaming Tool Execution

arXiv:2610.14659v1 Announce Type: new Abstract: Streaming speech assistants can launch tools before users finish speaking, overlapping execution with incoming speech to reduce user waiting time. Howev

Read the Room Before Your Model Generates

arXiv:2610.18820v2 Announce Type: new Abstract: Large language models (LLMs) generally rely on sampling temperature to regulate the amount of randomness in their outputs. Mainstream methods for select

RECAP: Calibrating Low-Bit Reasoning Models on the History They Read

arXiv:2610.15259v1 Announce Type: new Abstract: Low-bit quantization reduces the cost of serving reasoning models but can substantially degrade long chains of thought. Each step reads cached keys and

Revisiting Generative vs. Dense Retrieval: A Static Value Prior Reverses the Gap

arXiv:2610.19065v1 Announce Type: cross Abstract: Generative retrieval (GR) has emerged as a strong candidate generator for sequential recommendation as language models and semantic-item identifiers h

researcharxiv-cs-lg

Risky-Bench: Evaluating Agentic Safety Risks in Real-World Life-Assist Deployments

arXiv:2602.03100v2 Announce Type: replace Abstract: Large Language Models (LLMs) are increasingly deployed as agents that operate in real-world environments, introducing safety risks beyond linguistic

safetyarxiv-cs-ai

RM-OPD: On-Policy Distillation for Generative Reward Models

arXiv:2610.20174v1 Announce Type: cross Abstract: Reinforcement Learning on Mixed-domain preference data (Mix-RL) has become the mainstream approach for training general-purpose Generative Reward Mode

ROAR: Unifying Runs across Heterogeneous AI-Driven Research Systems

arXiv:2610.03966v2 Announce Type: replace Abstract: Each run of an AI-driven research system (ADRS) is an expensive search over a vast solution space, and dependable evaluation requires many runs, mak

RUBATO: Decoupled Clocks Allow Full-Duplex Models to Think as Fast as They Can

arXiv:2610.14299v1 Announce Type: cross Abstract: Recent Speech Full-Duplex (FDX) models are capable of engaging in naturalistic conversation by listening and speaking at the same time. Building on th

SAT-RL: Training Language Models for Logical Reasoning with Symbolic Solvers

arXiv:2610.14402v1 Announce Type: new Abstract: Large language models (LLMs) have achieved strong performance in mathematical and logical reasoning, yet they still suffer from the curse of complexity

Seeing, Maintaining, and Learning: The Evolution of Multimodal Agent Memory

arXiv:2610.19366v1 Announce Type: cross Abstract: Multimodal models and agents have emerged to enable AI systems to learn from and interact with the real world more like humans by integrating informat

SFT Leaves a Reproducible Signature in Activation Geometry: Calibrating Post-Training Interpretability Against Run-to-Run Noise

arXiv:2610.17093v1 Announce Type: new Abstract: A model diff credits post-training with whatever separates a base checkpoint from one post-trained descendant, presuming a rerun would land close by. On

Smart Alone, Blind Together: Why Accuracy Fails to CertifyTheory of Mind in Multi-Agent Teams

arXiv:2610.19051v1 Announce Type: cross Abstract: Coordinators that act on what they infer about teammates are usually certified by the accuracy of those inferences. We argue that accuracy is the wron

Specification-Guided Synthesis of Deadlock-Free Communication Protocol Refinements with Large Language Models

arXiv:2607.27964v3 Announce Type: replace-cross Abstract: Ensuring behavioural correctness in communication protocols is a central challenge in distributed software systems, as subtle inconsistencies

Structure-Preserving Document-Level Data Augmentation via Discourse Parsing

arXiv:2610.19497v1 Announce Type: new Abstract: Data augmentation (DA) plays a vital role in improving model performance in low-resource or limited-supervision scenarios within Natural Language Proces

researcharxiv-cs-cl

SwarmRSI: Recursive Self-Improvement through Evolving Scientific Agent Swarm

arXiv:2610.20431v1 Announce Type: cross Abstract: Recursive self-improvement (RSI) in scientific research aims to improve agents' research capabilities through their own experimental experience. Scien

agentsarxiv-cs-cl

Tail Competition Explains Scaling in Best-of-N Sampling for Verifiable Problems

arXiv:2610.19362v1 Announce Type: new Abstract: Best-of-N sampling is a simple inference-time scaling strategy for large language models, yet its behavior under incomplete reward models remains poorly

researcharxiv-cs-cl

The Future of AI-assisted Physics: Discovering, Verifying and Organizing Results via Formalization

arXiv:2610.14059v1 Announce Type: cross Abstract: Rapid advances in AI have opened a new era for physics, enabling unprecedented capabilities in research and discovery. However, these advances also br

The Geometry of Language Model Logits: Generic Structure and Learned Departures

arXiv:2610.18520v1 Announce Type: cross Abstract: Autoregressive language models produce, at every prediction step, a vocabulary-sized logit vector whose structure determines the conditional next-toke

Applications

Towards Training and Inference of Graph Neural Networks in Graph Databases

arXiv:2610.19142v1 Announce Type: new Abstract: Graph Neural Networks on large graphs commonly rely on in-memory graph storage with ad hoc data processing pipelines for both training and inference. Gr

applicationsarxiv-cs-lg

Tracing Morphology, Semantics, and Divergence in Language Acquisition

arXiv:2610.19041v1 Announce Type: cross Abstract: Understanding the dynamics of language acquisition in Large Language Models (LLMs) is crucial for uncovering how statistical representations evolve in

researcharxiv-cs-cl

Training a Compact, Strong, and Realistic Text-to-Speech Model

arXiv:2610.14776v1 Announce Type: cross Abstract: Text-to-speech (TTS) systems are conventionally optimized for intelligibility and speaker similarity. However, neither objective captures the prosodic

Unconstrained CTI-REALM with Open-Weights

arXiv:2610.14180v1 Announce Type: new Abstract: Organisations that want language-model agents for security work face a choice between open-weight models, which they can run and audit themselves, and f

Using Evidence-Centered Design to Guide Prompt Engineering in Measurement: An Illustration with Automated Essay Scoring

arXiv:2610.15231v1 Announce Type: new Abstract: Generative artificial intelligence (GenAI) models, particularly large language models (LLMs), are increasingly used in educational settings for tasks su

safetyarxiv-cs-ai

Vision: 'What Don't I Know About User's Preferences?' How Uncertainty Estimation Improves Proactive Assistant Alignment

arXiv:2610.15948v1 Announce Type: new Abstract: Proactive Smart Assistants (PSAs) increasingly use general knowledge, learned patterns from group-level data, patterns from individual user's data, and

safetyarxiv-cs-ai

Web3 x AI Agents: Landscape, Integrations, and Foundational Challenges

arXiv:2508.02773v4 Announce Type: replace-cross Abstract: The convergence of Web3 technologies and AI agents is a rapidly evolving frontier that is reshaping decentralized ecosystems. This paper prese

agentsarxiv-cs-ai

Weighted Ensemble Sampling for Rare-Event Estimation in Language Models

arXiv:2610.19202v1 Announce Type: new Abstract: Rare but severe failures of large language models can become practically significant at deployment scale. Estimating their probabilities with direct Mon

What Makes a Social Agent's Interactions Effective? Learning from Verifiable Outcomes in Paired Worlds

arXiv:2610.20195v1 Announce Type: new Abstract: Effective social interaction requires more than fluent responses: agents must reach plans that fit the participants' needs and circumstances. Existing e

When Occupation-derived Skills Don't Help: On the Limits of Skill-Augmented Career Path Prediction

arXiv:2610.20186v1 Announce Type: new Abstract: Skills are widely assumed to play a central role in career transitions, motivating their use in career path prediction (CPP) systems. In this work, we c

Who Should We Listen to More? Welfare-Aware Preference Acquisition for Pluralistic Alignment

arXiv:2610.19035v1 Announce Type: cross Abstract: Pluralistic alignment needs to accommodate diverse preferences under limited human feedback. Using AI simulators to label preference comparisons reduc

Local Ai

Why don't Graph Transformers attend to the same token?

arXiv:2610.19324v1 Announce Type: new Abstract: In large language models (LLMs), attention concentrates heavily on the first token. This 'attention sink' is believed to protect deep models against ove

local-aiarxiv-cs-lg

Agentic4PDM: Host-Governed Evidence Admissibility for Industrial Fault Diagnosis

arXiv:2610.17786v1 Announce Type: cross Abstract: We study industrial fault diagnosis as a classification task that assigns sensor time-series windows to predefined health states or fault types. Scarc

agentsarxiv-cs-lg

Ascent Conditions for Length-Normalized Single-Rollout Policy Updates

arXiv:2610.14670v1 Announce Type: new Abstract: Dividing each trajectory's score contribution by its realized length can change the direction of a reward-centered policy update. In a finite trajectory

Beyond a Better Score: Long-Horizon Agentic ML Development and Evaluation Protocol for Physics Time Series

arXiv:2610.14024v1 Announce Type: new Abstract: Physics time-series data are central to fundamental discoveries involving dark matter, neutrinos, and gravitational waves. Machine learning has shown st

CacheLink: Functional KV Transport for Zero-Prefill Cross-Model Context Handoff

arXiv:2610.15509v1 Announce Type: new Abstract: KV caches make long-context serving efficient by reusing past computation, but model switching breaks this reuse and forces the receiver to prefill the

researcharxiv-cs-lg

Hardware

DIGS: Distribution-Informed Gaussian Splatting for Training-Free Open-Vocabulary 3D Segmentation

arXiv:2610.16177v1 Announce Type: new Abstract: Open-vocabulary 3D Gaussian segmentation typically binds semantic features to the per-Gaussian state via per-scene gradient training (1-4 h) or the more

hardwarearxiv-cs-cv

Does System One Transfer? Calibrated Typed-Decision Models, Label Budgets, and Translation for Indonesian Regional Languages

arXiv:2610.14112v1 Announce Type: cross Abstract: Calibrated typed-decision ('System One') models return a label and a probability in a single forward pass at a small fraction of the cost of large lan

Applications

From a Large-Scale Teacher to Efficient Multimodal Students for Search Relevance Prediction

arXiv:2610.16569v1 Announce Type: cross Abstract: Relevance prediction between user queries and items is a fundamental component of e-commerce search systems. Traditionally, high-quality relevance lab

applicationsarxiv-cs-lg

Game-Theoretic Reinforcement Learning based Topology-Agnostic Defense for Virtual Power Plants

arXiv:2610.15183v1 Announce Type: new Abstract: The rapid proliferation of distributed energy resources (DERs) aggregated by virtual power plants (VPPs) elevates cybersecurity risk by expanding the at

safetyarxiv-cs-lg

Integrating 3D Posture Neutralization with Statistical Shape Modeling for Head-Neck-Shoulder Ergonomic Design

arXiv:2610.17671v1 Announce Type: cross Abstract: The design of ergonomic products and workspaces requires a deep understanding of human anatomy and posture. In particular, the head-neck-shoulder regi

researcharxiv-cs-cv

Interaction-as-a-Judge

arXiv:2610.17895v1 Announce Type: new Abstract: Coding agents are tasked with producing sophisticated interactive applications such as websites, desktop applications, and games. While humans assess th

safetyarxiv-cs-lg

Learning to Design SR Solvers: Harness Optimization for Symbolic Regression

arXiv:2610.16966v1 Announce Type: new Abstract: Symbolic regression (SR) seeks explicit equations that explain observed data, and recent methods increasingly employ large language models (LLMs) and ag

agentsarxiv-cs-lg

Tutorials

Learning to Detect Shifts in Unstructured Data Streams via Monitoring-by-Betting

arXiv:2610.17399v1 Announce Type: new Abstract: Continuous monitoring of generative AI systems requires detecting distribution shifts in the unstructured data they ingest and generate. Existing monito

tutorialsarxiv-cs-lg

LERR-Nav: Learning to Coordinate Exploration, Recovery, and Reminiscing for Object Navigation

arXiv:2610.15362v1 Announce Type: new Abstract: Object Navigation in unseen environments remains a fundamental challenge in embodied AI, as it requires agents to generalize visual and spatial reasonin

safetyarxiv-cs-ro

Local Ai

Linear-Time Non-Local Codec Placement for Machine Learning Model Compression

arXiv:2610.14703v1 Announce Type: new Abstract: On-device inference imposes constraints on model storage and operand traffic and thus benefits from compressed operand representations, which mandate th

local-aiarxiv-cs-lg

PRIME: Frequency-Aware Unified Pruning and Merging for MoE Compression

arXiv:2610.13982v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) models have emerged as an effective paradigm for scaling large language models, yet their massive parameter footprint poses sev