Search: “llm”

arXiv:2609.19199v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly adopted for compliance and legal reasoning tasks, yet their outputs often lack explicit grounding in leg

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Agents

CovR: Coverage-Aware Hardware Verification via Reasoning-Guided Reinforcement Learning

DGX agent

arXiv:2609.19189v1 Announce Type: cross Abstract: Design verification remains one of the most resource-intensive stages of hardware development, often consuming up to 70% of the total design effort. W

agentsarxiv-cs-cl

18 Sept 2026

Effective and Efficient Threat Hunting with Small Language Models

arXiv:2512.06660v3 Announce Type: replace-cross Abstract: Analysts in Security Operations Centers query massive telemetry streams using Kusto Query Language (KQL), but writing correct KQL demands spec

Efficiently Linking Unstructured Data for Multi-step Reasoning

arXiv:2609.19491v1 Announce Type: cross Abstract: Modern LLMs and AI agents increasingly support data engineering workflows that integrate evidence from unstructured sources. Such pipelines typically

EPIG-Tree: Compute-Optimal Branching for Gradient-Efficient Reinforcement Learning

arXiv:2609.20004v1 Announce Type: cross Abstract: Reward-based reinforcement learning for language models, exemplified by Group Relative Policy Optimization (GRPO), collapses an entire stochastic traj

local-aiarxiv-cs-ai

Evaluating Out-of-Distribution Robustness in Graph-Based Android Malware Classification: A New Principled Benchmark

model-releasesarxiv-cs-lg

arXiv:2508.06734v3 Announce Type: replace-cross Abstract: While graph-based Android malware classifiers report strong benchmark accuracy of over 94%, their performance sharply decreases up to 45% when

FakeSpotter: A content and strategy agnostic Viral Misinformation Detection Tool

arXiv:2609.19152v1 Announce Type: new Abstract: Misinformation detection tools often rely on binary true and false classifications or models trained on historical examples, limiting their usefulness w

researcharxiv-cs-cl

From Rollout to Reset: A Graph-Based Harness for Autonomous Long-Horizon Manipulation Evaluation

arXiv:2609.19413v1 Announce Type: cross Abstract: Robot manipulation policies are improving quickly, and real-robot evaluation remains the standard evidence for that progress. It still relies on a hum

GeLaCo: An Evolutionary Approach to Layer Compression

arXiv:2507.10059v2 Announce Type: replace Abstract: Large Language Models have achieved remarkable performance across a large number of tasks, but face critical deployment and usage barriers due to su

local-aiarxiv-cs-cl

How Far Can Sub-3B Open Language Models Go in Zero-Shot Essay Scoring on an 8 GB Consumer GPU?

arXiv:2609.20250v1 Announce Type: new Abstract: Zero-shot essay scoring with large language models is usually demonstrated with proprietary API models, yet the settings where automated scoring is most

local-aiarxiv-cs-lg

Language-model groups overstate consensus when replaying human deliberation on a reasoning task

arXiv:2609.20543v1 Announce Type: new Abstract: Full-consensus rates are often treated as indicators of collective cognition, yet depend on how participation and final states are operationalized. We r

Navigate or Relocate? Planning Among Movable Obstacles in Unknown Environments

arXiv:2609.19541v1 Announce Type: new Abstract: Conventional robot planning methods seek collision-free paths to a goal but fail when all paths are blocked. In these cases, the robot must determine wh

local-aiarxiv-cs-ro

QVAC Genesis III: A Large-Scale, High-Quality Open Synthetic STEM Corpus for Efficient Language Model Pre-Training

arXiv:2609.19513v1 Announce Type: new Abstract: High-quality pre-training data is a critical bottleneck for educational and STEM-specific language models targeting edge AI and on-device deployment whe

Redemption Score: A Multi-Modal Evaluation Framework for Image Captioning via Distributional, Perceptual, and Linguistic Signal Triangulation

arXiv:2505.16180v3 Announce Type: replace-cross Abstract: Evaluating image captions requires cohesive assessment of both visual semantics and language pragmatics, which is often not entirely captured

Schema-Anchored Latent Reasoning for Semantic Parsing-Based Knowledge Base Question Answering

arXiv:2609.20398v1 Announce Type: new Abstract: Semantic parsing (SP)-based knowledge base question answering aims to answer natural language questions by generating executable logical forms (LFs) ove

safetyarxiv-cs-cl

SCOUT: Sim-to-Real Text-Based Person Retrieval by Embedding-Space Prediction over Frozen Video Features

model-releasesarxiv-cs-cv

arXiv:2609.19483v1 Announce Type: new Abstract: Text-based person retrieval under a sim-to-real gap (synthetic training data, a real-image gallery) is usually tackled with costly fine-tuned cross-enco

Self Improvement via Fast Tree-search

arXiv:2609.19526v1 Announce Type: new Abstract: Coding agents can recursively modify their own implementations, forming a loop of self-improvement. While prior work shows this can boost performance on

Applications

Steering the Compass: Aligning Dynamic Psychological Counseling Conversations with Cognitive Behavioral Therapy Strategies

arXiv:2609.20565v1 Announce Type: new Abstract: Recent advancements in large language models have revolutionized the field of psychological counseling, especially in the context of Cognitive Behaviora

applicationsarxiv-cs-cl

Summarization Bias: The Directional Collapse of Objective Projection into Told-Mode Labels in Large Language Models --- A Conceptual Framework and Registered Test Protocol

arXiv:2609.20712v1 Announce Type: new Abstract: This paper introduces and operationalizes summarization bias: a proposed systematic tendency of large language models (LLMs) to represent narrative mean

safetyarxiv-cs-cl

The Complexity Kink: A Prompt-Side Structural Complexity Index for Code-Generation Reliability

arXiv:2609.19616v1 Announce Type: new Abstract: Complexity measured from generated code is failure-dependent: a difficult prompt can yield a short failing program and be assigned low output complexity

researcharxiv-cs-lg

Tutorials

The Life of a Token: from Words to Bits on the Wire

arXiv:2609.19924v1 Announce Type: cross Abstract: Large Language Models (LLMs) transform vast collections of unstructured text into semantic patterns used for language generation and reasoning tasks.

tutorialsarxiv-cs-lg

Bad Genius: Counterfactual-Guided Harness Evolution Beyond Task-Specific Shortcuts

arXiv:2609.18366v1 Announce Type: new Abstract: Reliable agent evaluation is complicated by automatic harness optimization, which repeatedly uses a released benchmark B_{rel} to guide a Proposer that

Behavioral Fingerprinting and Navigation Prediction in Web Browsing

arXiv:2609.18273v1 Announce Type: new Abstract: Web browsing often appears ephemeral: users visit a few websites, complete a task, and move on. However, even short fragments of browsing activity can c

researcharxiv-cs-lg

Benchmarking Large Language Models for Biomedical Relation Extraction

arXiv:2609.19071v1 Announce Type: new Abstract: Extracting SNP-phenotype associations from biomedical literature is vital but challenging. We benchmarked diverse NLP models, including MLMs, hybrid arc

Beyond Routine Compliance: Cunning Data Cultivates Safety Vigilance in Large Language Models

arXiv:2609.18515v1 Announce Type: new Abstract: Safety alignment teaches large language models (LLMs) to recognize harmful requests and reject risky instructions. Yet aligned models can fail when harm

Beyond Static RAG: An Adaptive, Tri-Metric Routing Framework for Efficient Long-Context Inference on Commodity GPUs

arXiv:2609.17564v1 Announce Type: new Abstract: Deploying retrieval-augmented generation (RAG) on commodity GPUs such as the NVIDIA T4 (16 GB VRAM) exposes a practical failure mode we call the Compres

local-aiarxiv-cs-lg

Can MiniMax-H3 Reason About the Physical World? An Evaluation of Omni-Modal Generative Model

arXiv:2609.18323v1 Announce Type: new Abstract: Recent Omni-Modal Generative Models (Omni-Models) have advanced content generation toward unified modeling of text, images, video, and audio. MiniMax-H3

safetyarxiv-cs-cv

Colla-Q: Toward Collaborative Experts in MoE Quantization via Minimax Precision Balancing

arXiv:2609.18131v1 Announce Type: cross Abstract: In this paper, we present a Mixture-of-Experts (MoE) quantization method based on activation entropy. Although quantization reduces memory and computa

researcharxiv-cs-cl

Collaborative Memory for Multi-Agent VLM Systems

arXiv:2609.17921v1 Announce Type: new Abstract: Vision-language model (VLM) agents combine specialized perception, tools, and reasoning to address complex visual tasks. In multi-agent settings, differ

CompArt: Operationalizing Aesthetic Alignment in Text-to-Image Generation via Principles of Art

arXiv:2503.12018v2 Announce Type: replace-cross Abstract: Text-to-Image (T2I) diffusion models have made rapid progress on semantic alignment (generating what is described in the prompt), yet users st

Does AI Assistance Leave a Temporal Fingerprint? Detecting Overreliance in AI-Assisted Writing and Programming

arXiv:2609.17883v1 Announce Type: cross Abstract: The rapid adoption of generative AI has made final artifacts unreliable evidence of student learning, and AI detectors that examine only the finished

researcharxiv-cs-ai

Hardware

Fathom: Per-Query Read Depth for Sparse Decoding over Offloaded KV Caches

arXiv:2609.17652v1 Announce Type: cross Abstract: When agentic sessions run to a million tokens with many sessions resident at once, the KV cache and the index that ranks it live in host memory, and t

hardwarearxiv-cs-cl

Flag Game: A Toy Model for Mechanistic Swarm Interpretability

arXiv:2609.19124v1 Announce Type: new Abstract: Emergent coordinated behaviors of AI agents are starting to present critical safety risks. A key phenomenon driving these behaviors is the rapid formati

I code or AI code: A comparative evaluation of AI-rated scores in classroom observations

arXiv:2609.18274v1 Announce Type: cross Abstract: Classroom observations are widely recognized as a key tool for establishing benchmarks of education quality and guiding pedagogical improvement, yet t

Knowledge-Graph Based Augmentation versus Retrieval Augmented Generation for Cultural-Related Question Answering

arXiv:2609.18317v1 Announce Type: cross Abstract: Large language models (LLMs) suffer from a long-tail deficit: culturally specific facts, particularly those concerning underrepresented regions such a

Label-Confidence-Aware Uncertainty Estimation in Natural Language Generation

arXiv:2412.07255v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) demonstrate remarkable capabilities in generative tasks but pose potential risks due to their tendency to generat

Mem2Ego: Empowering Vision-Language Models with Global-to-Ego Memory for Long-Horizon Embodied Navigation

arXiv:2502.14254v3 Announce Type: replace-cross Abstract: Recent advancements in Large Language Models (LLMs) and Vision-Language Models (VLMs) have made them powerful tools in embodied navigation, en

MudawanSn: A Gold-Standard Wolof-Arabic Parallel Corpus for Machine Translation

arXiv:2609.17539v1 Announce Type: new Abstract: We present MudawanSn, a gold-standard resource of 1,271 sentence-aligned pairs manually translated from Wolof into Modern Standard Arabic (MSA). The sou

OBC-Prune: Outcome-Based Calibration for Large Reasoning Model Pruning

arXiv:2609.17890v1 Announce Type: new Abstract: Large reasoning models (LRMs) generate long chain-of-thought traces before answering, creating significant inference overhead. Pruning can reduce this c

Playing log(N)-Questions over Wikipedia Abstracts: Communication Efficiency Between Paired Frontier Models

arXiv:2609.19113v1 Announce Type: new Abstract: We evaluate six frontier language models on the two-agent log(N)-Questions game. A questioner sees N Wikipedia lead paragraphs and must identify a secre

PULSE: Unlocking Practical Image Compression on Single-Thread CPU

arXiv:2609.18602v1 Announce Type: new Abstract: Despite recent progress in learned image compression, existing methods remain computationally expensive on resource-constrained hardware, particularly C

agentsarxiv-cs-cv

REPAIR: Resolving Long-Tail Confusion in Scientific Retrievers via Fact-Verified Iterative Refinement

arXiv:2609.18262v1 Announce Type: new Abstract: Precise retrieval of scientific information is fundamentally constrained by long-tailed concepts and high fact-sensitivity of scientific corpora. These

researcharxiv-cs-ai

RideWay: Benchmarking Efficient Task Completion for Tool-Using Language Agents

arXiv:2609.17985v1 Announce Type: new Abstract: AI agents are usually evaluated by whether they complete a task. In interactive service settings, a successful agent can still frustrate users by asking

Selection Is Retrieval, Abstention Is Not: On-Device Tool Routing over 70 Korean-English Actions

arXiv:2609.18672v1 Announce Type: new Abstract: An AI assistant that calls tools makes two decisions on every request: which tool to invoke, and whether any available tool applies. In the usual design

local-aiarxiv-cs-cl

SNOMED CT Concept Recommendation from Masked Clinical Context

arXiv:2609.17855v1 Announce Type: new Abstract: Standardizing clinical language to SNOMED CT supports interoperability, analytics, and reusable phenotyping, but concept recommendation remains difficul

TeleAntiFraud 2.0: A Refreshable, Profile-Grounded, and Audio-Based Benchmark for Telecom Fraud Detection

arXiv:2609.18748v1 Announce Type: cross Abstract: Telecom fraud scripts evolve rapidly and are often designed to resemble routine service conversations, creating two key requirements for audio-based t

Training-Adaptive Convolutional Sparse Coding via Information Bottleneck for Robust Visual Representation

arXiv:2609.19122v2 Announce Type: new Abstract: Visual signals require compact yet sufficient representations for robust downstream prediction. Convolutional sparse coding (CSC) provides an explicit m

researcharxiv-cs-cv

TuiML: Machine Learning for AI Agents

arXiv:2609.17984v1 Announce Type: new Abstract: Machine-learning libraries such as Weka and scikit-learn were designed for human programmers. Language-model agents now use these same libraries by reca