Search: “reasoning”

FOCUS & RePAIR: Mitigating Text Degeneration via Token-Level Guidance for Pruned Large Language Models

arXiv:2608.26676v1 Announce Type: cross Abstract: Pruning is a practical approach to compress large language models (LLMs), but it can amplify text degeneration, especially repetition loops, even when

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HybridProver: Augmenting Theorem Proving with LLM-Driven Proof Synthesis and Refinement

DGX agent

arXiv:2505.15740v2 Announce Type: replace-cross Abstract: Formal methods play a crucial role in ensuring the reliability of critical systems through rigorous mathematical verification. However, their

model-releasesarxiv-cs-ai

28 Aug 2026

JudgeStealer: Extracting LLM Judging Capabilities across Evaluation Protocols

arXiv:2608.26982v1 Announce Type: new Abstract: Large language model (LLM) judges are increasingly used across various evaluation scenarios, making their judgment capabilities valuable intellectual pr

Learning the ARTS of Search for Automated Discovery

arXiv:2606.21891v2 Announce Type: replace Abstract: Scientific discovery can be formulated as an iterative search process over the space of hypotheses and experiments. Contemporary methods navigate th

LLMs for Academic Workflows: An Evaluation of Literature Reviews Generated with Short and Long Context Windows of LLMs

arXiv:2608.26145v1 Announce Type: new Abstract: Our research focuses on evaluating literature reviews generated in short and long context settings of large language models (LLMs) to investigate the im

MedFG-VQA: Low-Frequency Memory and Graph Attention for Lightweight Medical VQA

arXiv:2608.26848v1 Announce Type: cross Abstract: Medical Visual Question Answering (Med-VQA) holds significant promise for clinical decision support, yet faces challenges due to limited annotated dat

PACE: A Unified Condense-and-Extract Paradigm for Fast VLM Inference

arXiv:2608.27206v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) demonstrate exceptional visual reasoning capabilities, yet their inference costs escalate rapidly with the proliferation

Predicting Consequences and Reinforcing Navigation Policies with Latent World Models

arXiv:2608.26190v1 Announce Type: new Abstract: World models enable agents to reason about future outcomes and learn policies from their knowledge of state transition, but existing approaches primaril

Risks and Controls for Multi-Agent Systems: an analytical framework for deployment of AI agents across organisational boundaries

arXiv:2608.26626v1 Announce Type: cross Abstract: This report presents a framework to help organisations, policymakers and researchers reason about the risks that emerge when AI agents interact with e

agentsarxiv-cs-ai

SAREF-based Ontology for Distributed AI Workflows across the Edge-Fog-Cloud Continuum

arXiv:2608.26160v1 Announce Type: new Abstract: Nowadays semantic models provide limited support for representing distributed AI workflows and their execution across heterogeneous edge, fog, and cloud

SPAR-K: Scheduled Periodic Alternating Early Exit for Spoken Language Models

arXiv:2603.09215v2 Announce Type: replace Abstract: Interleaved spoken language models (SLMs) alternately generate text and speech tokens, but decoding at full transformer depth for every step becomes

Syntax vs. Semantics: How Transformers Learn Deep Dependencies

arXiv:2608.26139v1 Announce Type: cross Abstract: Large Language Models demonstrate remarkable syntactic fluency, yet the optimization dynamics governing their acquisition of deep semantic dependencie

TransMeme: A Multi-Agent Framework for Cross-Cultural Meme Transcreation

arXiv:2608.27127v1 Announce Type: new Abstract: Internet memes are a pervasive form of multimodal online communication; however, such communication often involves users from diverse linguistic and cul

CoRE: Weakly Supervised Coarse-to-Fine Risk Evidence Learning in Driving Videos

arXiv:2608.25344v1 Announce Type: new Abstract: Perceived risk in driving evolves over time and may be supported by specific scene entities, yet supervision is typically limited to coarse video-level

Local Ai

CVE-SAI: Counterfactual Visual Evidence-Guided Selective Attribute Indexing for Risk-Controlled E-commerce Search

arXiv:2608.25023v1 Announce Type: cross Abstract: Multimodal product models can complete missing e-commerce attributes, yet current methods still optimize attribute-answer accuracy without verifying v

local-aiarxiv-cs-cv

From Memorization to Absorption: Mixed-Policy RL for Continual Knowledge Injection

arXiv:2608.25243v1 Announce Type: new Abstract: Continual knowledge injection is essential for keeping large language models up-to-date in a fast-evolving world. Existing methods rely on supervised fi

safetyarxiv-cs-cl

Mind2Report: Expert-Level Commercial Report Synthesis via Cognitive Deep Research Agent

model-releasesarxiv-cs-cl

arXiv:2601.04879v2 Announce Type: replace Abstract: Synthesizing informative commercial reports from massive and noisy web sources is critical for high-stakes business decisions. Although recent deep

One Symptom, Three Levers: A Critical Review of On-Policy Self-Distillation

arXiv:2608.25936v1 Announce Type: cross Abstract: On-policy distillation trains a language model on its own generations while a teacher scores them token by token. It combines the dense supervision of

safetyarxiv-cs-cl

PlanSightRAG: A Visual-First Multimodal RAG for Automating Question Answering and Compliance Checking for Civil Standard Plans

model-releasesarxiv-cs-cl

arXiv:2608.26091v1 Announce Type: cross Abstract: Civil infrastructure compliance checking has long relied on engineers manually reading legacy 2D plans; however, OCR-based automation strips away the

Query-Side Attacks on GNN-Based KGQA: Tracing Failures from Entity Linking to Answer Generation

model-releasesarxiv-cs-cl

arXiv:2608.25922v1 Announce Type: new Abstract: GNN-based Knowledge Graph Question Answering (KGQA) pipelines process queries through four discrete stages: entity linking, subgraph retrieval, GNN reas

StreamPI: Streaming Multimodal Temporal Modeling for Vision-Language-Action Models

arXiv:2608.26067v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have demonstrated effectiveness in robot manipulation, yet state-of-the-art models such as pi0.5 operate under a sin

TailSFT: Filtered Fine-Tuning Improves Post-Training Performance

arXiv:2608.25756v1 Announce Type: new Abstract: Reinforcement learning post-training drives reasoning and agentic capabilities in modern AI systems, yet a growing body of work shows that it is most ef

agentsarxiv-cs-lg

Tool Verification for Test-Time Reinforcement Learning

arXiv:2603.02203v2 Announce Type: replace-cross Abstract: Test-time reinforcement learning (TTRL) has emerged as a promising paradigm for Recursive Self-Improving AI (RSI) by adapting Large Reasoning

V-Rubrics: Visual Faithfulness via Rubric-Based Reinforcement Learning

arXiv:2608.25580v1 Announce Type: new Abstract: Vision-language models can produce fluent answers that are insufficiently grounded in the visual evidence: a single unsupported object, chart value, or

A Human-Factors Guided Cognitive Model of Visuospatial Complexity in Embodied Active Vision

arXiv:2608.23572v1 Announce Type: cross Abstract: We propose a novel framework for the analysis of multimodal data -- encompassing visual, auditory, and spatial stimuli -- foregrounding the role of co

A Mathematical Theory of Interpretation: Rational Entropy, Spectral Readout, and Confusability as a Resource

arXiv:2608.23892v1 Announce Type: cross Abstract: This article presents the abridged core of A Mathematical Theory of Interpretation (MTI), which treats interpretation as observer-relative spectral me

Can Large Language Models Still Explain Themselves? Investigating the Impact of Quantization on Self-Explanations

arXiv:2601.00282v2 Announce Type: replace-cross Abstract: Quantization is widely used to accelerate inference and streamline the deployment of large language models (LLMs), yet its effects on self-exp

Local Ai

Contrastive Branch Policy Optimization

arXiv:2608.24300v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) enables language models to learn multi-turn interaction with external tools, yet its sparse outc

local-aiarxiv-cs-ai

Design-to-Plan: A Large Language Model-Based Multi-Agent Framework for Manufacturing Process Planning from 3D CAD Models and 2D Engineering Drawings

arXiv:2608.24039v1 Announce Type: cross Abstract: Manufacturing process planning transforms heterogeneous design information into coherent manufacturing decisions. However, existing approaches focus o

Don't Just Listen, Try Planning: Graph-based Retrieval-Generation Agent for Long-form Audio Meeting Understanding

arXiv:2608.24048v1 Announce Type: cross Abstract: While long-form audio meeting understanding (LAMU) is garnering growing attention, task-specific question answering (QA) datasets remain scarce. Exist

agentsarxiv-cs-ai

EstLLM: Enhancing Estonian Capabilities in Multilingual LLMs via Continued Pretraining and Post-Training

arXiv:2603.02041v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are predominantly trained on English-centric data, resulting in uneven performance for smaller languages. We stud

Evaluating Language Models on Cross-Language Code Functional Equivalence

arXiv:2608.23961v1 Announce Type: cross Abstract: Background: Large Language Models (LLMs) have demonstrated strong performance across a variety of code-understanding tasks, leading many to believe th

Granite.Trust Policy Tools: Shareable, Actionable Policies for Generative AI Applications

arXiv:2608.23870v1 Announce Type: new Abstract: When it comes to safety policies for generative AI, one size does not fit all. Each organization and use case needs to mitigate different risks dependin

Graph-Supervised Hierarchical Clinical Alignment for Radiology Report Generation with Large Language Models

arXiv:2608.24121v1 Announce Type: new Abstract: Radiology report generation (RRG) has recently benefited from large language models, which substantially improve report fluency. However, clinically fai

safetyarxiv-cs-cv

Hybrid Semantic Tool Discovery for Enterprise MCP Gateway: Architecture and Implementation

arXiv:2608.23992v1 Announce Type: cross Abstract: Large language model (LLM) agents invoke external tools to retrieve and reason over information beyond pretrained knowledge. The Model Context Protoco

LUX: A Lesion-Aware Graph-Conditioned Visual - Language Architecture for Explainable Endoscopic Captioning

arXiv:2608.23853v1 Announce Type: new Abstract: The interpretation of endoscopic imagery in ulcerative colitis is complex and subjective, with variability in human assessment and subtle mucosal inflam

safetyarxiv-cs-cv

Single State Update Predictive Coding training for Time Series Forecasting and Anomaly Detection

arXiv:2608.24697v1 Announce Type: new Abstract: Predictive Coding (PC) is a neural learning paradigm that enables parallelizable neural network layer updates. However, the main bottleneck of PC Networ

researcharxiv-cs-lg

The Shadow Price of Intelligence: Quality Degradation in LLM Inference as a Supply Chain Problem

arXiv:2608.23986v1 Announce Type: cross Abstract: Large language model providers are compute constrained, and their universal response to congestion is to degrade service: route queries to smaller mod

Tutorials

UCO: A Multi-Turn Interactive Reinforcement Learning Method for Adaptive Teaching with Large Language Models

arXiv:2511.08873v3 Announce Type: replace Abstract: Large language models (LLMs) are shifting from answer providers to intelligent tutors in educational settings, yet current supervised fine-tuning me

tutorialsarxiv-cs-ai

VisCache: Visual KV Cache Pruning for Efficient Vision Large Language Model Inference

arXiv:2608.24063v1 Announce Type: cross Abstract: While Vision Large Language Models (VLLMs) have achieved remarkable success in multimodal reasoning, their long-context inference remains prohibitivel

Applications

When Seeing Is Not Enough: Benchmarking Interactive Visual Grounding in LVLMs

arXiv:2608.23978v1 Announce Type: new Abstract: Visual grounding is typically evaluated as a one-shot mapping from an informative referring expression to a visual target. This formulation misses a cen

applicationsarxiv-cs-ai

ADHint: Adaptive Hints with Difficulty Priors for Reinforcement Learning

arXiv:2512.13095v3 Announce Type: replace Abstract: To address the limited capability expansion and low sample efficiency of Reinforcement Learning (RL), recent methods have integrated ''hints'' into

Apodex 1.1: Scaling Agentic Intelligence for Complex Work

arXiv:2608.23283v1 Announce Type: new Abstract: General-purpose language models can reason and synthesize knowledge, but complex work also requires sustained interaction with files, information source

Benchmarking Retrieval-Augmented Generation Strategies for Large Language Model-Based Travel Mode Choice Prediction

arXiv:2508.17527v2 Announce Type: replace Abstract: Accurately predicting travel mode choice is essential for effective transportation planning, yet traditional statistical and machine learning models

ChemDIRT: A Diversified Instruction, Representation, and Task Benchmark for Robust Chemistry-LLM Evaluation

model-releasesarxiv-cs-lg

arXiv:2608.21504v1 Announce Type: new Abstract: The rapid advancement of large language models (LLMs) has led to increasing interest in their application to scientific domains such as chemistry. Howev

Dual-Grained Agent Memory and Shapley Context Attribution for Multimodal Agentic Learner

arXiv:2608.23268v1 Announce Type: new Abstract: Frontier multimodal large language models (MLLMs) deliver impressive perception yet still falter on scientific and mathematical reasoning. Parameter-lev

Geo-VLA: Geometry-Aware Vision-Language-Action Planning via Internalization of Map Semantics

arXiv:2608.21440v1 Announce Type: cross Abstract: Vision-language-action (VLA) models have advanced end-to-end autonomous driving by leveraging foundation models for semantic reasoning and long-tail g

agentsarxiv-cs-ai

Last But Not Least: Boundary Attention CalibratiON for Multimodal KV Cache Compression

arXiv:2606.14782v3 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) achieve strong vision-language reasoning but incur large KV caches and high decoding latency with lon