Search: “gpt”

Stream Recursion Model (SRM)

arXiv:2609.28809v1 Announce Type: new Abstract: Mechanistic interpretability seeks to make verifiable statements about the internal behavior of large language models (LLMs). Many interpretability tech

25 Sept 2026

The Last Human Gate: Forward Deployed Engineering for Governance Automation

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arXiv:2609.29345v1 Announce Type: new Abstract: Enterprise governance requires decisions, evidence, and accountable authority; it does not require every review task to retain its current human impleme

model-releasesarxiv-cs-ai

25 Sept 2026

COMED: The Missing Middle Between Routing and Collaboration in Multi-LLM Inference

arXiv:2609.26913v1 Announce Type: cross Abstract: No single Large Language Model (LLM) is uniformly reliable across queries, motivating multi-model inference systems that either route among models or

Computation Over Geometry: Meaning Identity Is Computed, Not Shipped in the Embeddings

arXiv:2609.28290v1 Announce Type: new Abstract: Meaning identity (whether two sentences say the same thing after wording changes) is treated in retrieval and RAG as a geometric fact about independentl

Delegated Misalignment: How Multi-Agent Structures Amplify LLM Safety Risks

arXiv:2609.27900v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed in multi-agent systems where a principal agent decomposes tasks and delegates them to subordinate

Forecast Workflow Bench: Evaluating Language-Model Decisions with Budgeted Forecast Tools

arXiv:2609.27385v1 Announce Type: cross Abstract: Time-series foundation models (TSFMs) provide forecasts for operational decisions, but accuracy alone does not determine their value. Evaluating agent

researcharxiv-cs-ai

Guides That Cause Actions: An Offline Study of Guide-Action Mutual Reinforcement in Multimodal Web Agents

arXiv:2609.27353v1 Announce Type: new Abstract: Web agents are usually evaluated in live environments, where environment state and judge models drift between runs, so the same checkpoint rarely reprod

Improving LLM-based Autonomous Web Agents with Filtering

arXiv:2609.27770v1 Announce Type: new Abstract: Autonomous web agents, powered by Large Language Models (LLMs), have garnered significant attention for automating various web-based tasks with multi-st

Provably Complete Generalized Planning with LLMs

arXiv:2609.27105v1 Announce Type: new Abstract: Generalized planning aims to compute a plan that solves all instances of a planning domain. Recent work has used LLMs to automatically generate and debu

Recognized but Not Produced: A Generation Benchmark for Culturally Specific Kinship Terms

arXiv:2609.26942v1 Announce Type: cross Abstract: Current literature evaluates large language models (LLMs) on multilingual kinship understanding using multiple choice benchmarks, treating it as a rec

SkillGym: Internalizing Human Skills into LLMs for Real-World Problem Solving

arXiv:2609.27717v1 Announce Type: new Abstract: Human-written agent skills encode rich workflows for real-world problem solving, but are typically used as external inference-time instructions rather t

TwinCheck: Evidence-Grounded Negative-Twin Verification for Stateful Tool Agents

arXiv:2609.26911v1 Announce Type: new Abstract: A single locally plausible tool call can derail an otherwise successful agent trajectory. Suspicion alone does not justify intervention, because the rep

Validation and Simulation Catch Different Errors: Four Levels of Evaluation for LLM-Generated Circuits

arXiv:2609.26830v1 Announce Type: cross Abstract: Simulation success is not equivalent to structural correctness for LLM-generated circuits. We define and measure four evaluation levels -- schema vali

What Looks Like a Capability Limit in Vision-Language Models Is a Readout Limit

arXiv:2609.27408v1 Announce Type: cross Abstract: Benchmarks for vision-language models offer their answer choices in some convention: a letter, a color name, a pixel coordinate. That convention is tr

Agensh: Scaling Organizational Intelligence to 1,024 Agents

arXiv:2609.26781v1 Announce Type: new Abstract: A multi-agent system can reduce latency on complex tasks by executing work concurrently. Several pioneering harness frameworks support multi-agent syste

Capable yet Parsimonious: Extracting and Characterizing Hidden Chain-of-Thought in Frontier Models

arXiv:2609.26637v1 Announce Type: new Abstract: The rapid capability gains of frontier language models are widely attributed to improved reasoning abilities, yet this cannot be verified as raw CoT tra

CliffCompaction: Cost-Efficient Compaction for Long-Horizon Coding Agents

arXiv:2609.26779v1 Announce Type: cross Abstract: Agents often work on complex problems that require millions of tokens of context, which necessitates compacting across sessions due to limited context

Component Type, Not Reconstruction Error, Predicts Attention Quantization Sensitivity

arXiv:2609.26173v1 Announce Type: new Abstract: Many post-training quantization (PTQ) methods use layer-wise reconstruction, second-order proxy objectives, or activation-aware transformations to reduc

Do Synthetic Personas Predict Real Audience Response? A Sim-to-Real Study Where a No-Persona Baseline Beats Persona-Based Copy Simulation

arXiv:2609.25010v1 Announce Type: cross Abstract: Marketers increasingly use large language models (LLMs) as 'synthetic personas' to predict how an audience will react to a piece of copy before it shi

FinFIRST: Benchmarking Search Agents for Financial Information Retrieval, Sourcing and Traceability

arXiv:2609.25192v1 Announce Type: new Abstract: Financial search is a highly demanding task for LLM agents, requiring not only a correct final answer but also temporally valid information retrieval, a

FinInteract: Benchmarking Clarification and Intent Integration in Ambiguous Financial Question Answering

arXiv:2609.24002v1 Announce Type: new Abstract: Large language model agents increasingly answer financial questions by searching regulatory filings. Such questions are often deceptively under-specifie

FIRE: Failure-Informed Runtime Engineering for Reliable Language-Model Agents

arXiv:2609.26048v1 Announce Type: cross Abstract: Language-model agents often reach a working solution and then fail to consistently deliver it. We study runtime policies: targeted natural-language in

FrontierMath Erdos

arXiv:2609.25050v1 Announce Type: new Abstract: We introduce FrontierMath Erdos (FME), a benchmark of 68 Erdos problems that are open as of August 2026. To solve a task in FME, AI systems must resolve

Safety

HARMONY: Hierarchical Agentic Reasoning for MONocular Image-to-Scene Synthesis

arXiv:2609.26793v1 Announce Type: new Abstract: Compositional 3D scene reconstruction has recently been explored from two directions: agentic reasoning that provides semantic understanding of spatial

safetyarxiv-cs-cv

Pinocchio: Fast Uncertainty Estimates for Black-Box Language Models

arXiv:2609.24881v2 Announce Type: new Abstract: In high-stakes decision-making applications of large language models (LLMs), practitioners require not only accurate LLMs but also uncertainty estimates

researcharxiv-cs-ai

Reasoning Topology Matters: A Controlled Study of LLM-Based Cybersecurity Analysis

arXiv:2609.24710v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used in cybersecurity, where accurate analysis often requires multi-step and context-dependent reasoning

SPECTRA: Adaptive Execution of Speculative Decoding on a Runtime-Reconfigurable Tiled Architecture

arXiv:2609.24847v1 Announce Type: cross Abstract: LLM inference on edge devices is constrained by computational and memory resources, making efficient autoregressive decoding challenging. Speculative

researcharxiv-cs-ai

Syndrome, Synergy, and Safety: Structured Reasoning and Knowledge-Driven Alignment for TCM Prescription Generation

arXiv:2609.25755v1 Announce Type: new Abstract: Applying large language models to Traditional Chinese Medicine (TCM) prescription generation reveals three clinically critical gaps: models produce end-

Terminal Shrinkage Averaging Reveals a Schedule-Estimator Interaction in LLM Pretraining

arXiv:2609.25482v1 Announce Type: new Abstract: Large language model (LLM) pretraining conventionally returns the raw final iterate. This couples two design choices: the learning-rate schedule that ge

Trains but Doesn't Learn: A Post-Training Delivery Benchmark for LLM Agents as Forward-Deployed Engineers

arXiv:2609.25237v1 Announce Type: cross Abstract: Post-training is becoming a service (PTaaS): a customer hands an operator data and a goal, and a forward-deployed engineer (FDE) returns a fine-tuned,

VLM-in-Sandbox: Visual Workspaces for Agentic Visual Reasoning

arXiv:2609.24362v1 Announce Type: new Abstract: Sandboxed computer environments support multi-step reasoning with tools, executable programs, and persistent files, yet their extension from language mo

What is the Difference Between Me and You? Benchmarking the Quality Gap Between Human-Written and AI-Generated Code

arXiv:2609.12708v2 Announce Type: replace-cross Abstract: AI coding assistants are becoming co-authors of production software, yet their evaluation centers on functional correctness, leaving open whet

Agents

A Pinch of SFT, A Dash of RL: When Reinforcement Learning Helps Long-Horizon Advertising Agents

arXiv:2609.22194v1 Announce Type: new Abstract: Enterprise analytics agents solve long-horizon tool-use problems over distributed business data, requiring retrieval, reasoning, API calls, code executi

agentsarxiv-cs-lg

An Empirical Cost Attribution of Context-Compression Gateways in Multi-Turn Coding Agents

arXiv:2609.22114v1 Announce Type: new Abstract: Context compression is widely proposed as a way to cut the token bill of LLM coding agents, and public benchmarks report that aggressive compression pre

Auditing Political Alignment in LLM Assistants: Engagement, Stance, and User Identity

arXiv:2609.23039v1 Announce Type: new Abstract: LLM-based AI systems answer political questions for hundreds of millions of people. Current audits measure what they say to an average user, but their b

Beyond Accuracy and Surface Fluency: Risk-Sensitive Evaluation of LLMs for Legal Clause Generation

arXiv:2609.22127v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to draft contractual language, yet conventional accuracy or preference-based evaluations are poorly m

Document Retrieval-Aware Chunking (D-RAC): Universal Retrieval-Aware Ingestion of Enterprise Documents via PDF Normalization and Multimodal Markdown Conversion

model-releasesarxiv-cs-cv

arXiv:2609.24220v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) systems over enterprise knowledge bases must ingest heterogeneous document formats -- PDFs, Word documents, present

EmoPose: Vision-Language Model Guided Emotion-Aware Gesture Generation for Humanoid Robots

model-releasesarxiv-cs-ro

arXiv:2609.23414v1 Announce Type: new Abstract: Socially competent humanoid robots must communicate affect and intent through gesture as well as speech, yet open-ended interaction must become motion t

ESLM: Risk-Averse Selective Language Modeling for Efficient Pretraining

arXiv:2505.19893v2 Announce Type: replace-cross Abstract: Large language model pretraining is compute-intensive, yet many tokens contribute marginally to learning, resulting in inefficiency. We introd

researcharxiv-cs-cl

Explain Less, Understand More: Data-Efficient Personalization of Reader-Dependent Jargons

arXiv:2505.16227v4 Announce Type: replace Abstract: Personalizing jargon detection and explanation is essential for making technical documents accessible to readers with diverse disciplinary backgroun

Fairness Beyond Anonymization? Demographic Leakage in German LLM-Generated Resumes

arXiv:2609.22188v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly integrated into AI-assisted hiring pipelines, including automated resume generation and screening. Under t

Fathom-Vaidya: Advancing Medical Reasoning with Rubric-Based Rewards

arXiv:2609.24480v1 Announce Type: cross Abstract: Deploying Large Language Models (LLMs) in healthcare requires robust performance across two complementary dimensions - diagnostic reasoning: the conve

From Tables to Quantified Statements: Evaluating LLM Inference Generation through Executable Verification

arXiv:2609.23966v1 Announce Type: new Abstract: LLMs can generate fluent descriptions from tables, but their outputs may remain logically unsupported by the structured data. We introduce STAT-TO-TEXT,

researcharxiv-cs-cl

Machine-Interpretable Information: Compiling Documents into Searchable and Readable Protocol States

arXiv:2609.23371v1 Announce Type: new Abstract: Long-context language models interface with external knowledge through raw natural language. In retrieval-augmented systems, this creates a persistent i

Math2Visual-X: A Modular Framework for Pedagogically Aligned Lower-Primary Math Visuals Generation

arXiv:2609.22647v1 Announce Type: new Abstract: Visual representations can help lower-primary learners understand Math Word Problems, but generating classroom-usable visuals remains difficult. Existin

researcharxiv-cs-cv

Memento No More: Coaching AI Agents to Master Multiple Tasks via Hints Internalization

arXiv:2502.01562v3 Announce Type: replace Abstract: As the general capabilities of artificial intelligence (AI) agents continue to evolve, their ability to learn to master multiple complex tasks throu

Memory vs. Context? Influential Factors of Factual Recall in Language Models

arXiv:2609.24238v1 Announce Type: new Abstract: We reproduce and stress-test the work of Yu et al. (2023), who characterize how language models (LMs) arbitrate between memorized knowledge and contradi

researcharxiv-cs-cl

MolSC: Leveraging Substituent Contributions to Enhance Fine-grained Molecular Understanding in LLMs

arXiv:2609.23073v1 Announce Type: new Abstract: Recent advances in natural language processing have led to molecular Large Language Models (LLMs) with strong performance across diverse chemistry tasks