Search: “gpt”

When Claws Remember but Do Not Tell: Stealthy Memory Injection in Persistent Personal Agents

arXiv:2607.05189v1 Announce Type: cross Abstract: Persistent personal agents combine long-term memory with access to users' external environments, enabling personalized foreground assistance and proac

7 Jul 2026

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Model Releases

Which Algorithm Specification Formats Help Language Models Implement Machine Learning Algorithms?

DGX agent

arXiv:2607.03158v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used to implement algorithms from research manuscripts, but papers often leave implementation choices im

model-releasesarxiv-cs-ai

7 Jul 2026

AgenticRAGTracer: A Hop-Aware Benchmark for Diagnosing Multi-Step Retrieval Reasoning in Agentic RAG

arXiv:2602.19127v2 Announce Type: replace Abstract: With the rapid advancement of agent-based methods in recent years, Agentic RAG has undoubtedly become an important research direction. Multi-hop rea

Ask the Right Comparison:Bias-Aware Bayesian Active Top-k Ranking with LLM Judges

arXiv:2607.02104v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as cheap, scalable judges that compare candidate outputs pairwise -- to rank responses, select models

Automated grading of Linux/bash examinations using large language models: a four-level cognitive taxonomy approach

arXiv:2607.02432v1 Announce Type: new Abstract: Scalable and reliable grading of command-line examinations remains a challenge in computing education, where rising enrolments make manual marking diffi

Beyond Skepticism: Evaluating LLMs Pedagogical Intent Reasoning with the Adaptive Pedagogical Vigilance Framework

arXiv:2607.01581v1 Announce Type: new Abstract: The capacity of Large Language Models (LLMs) to reason about pedagogical intent within instructional communication remains underexplored, particularly i

BOUNDARY_SYNC: Measuring Communication-Induced Representational Coupling in Multi-Agent LLM Systems

arXiv:2607.01600v1 Announce Type: cross Abstract: As large language models (LLMs) are deployed as communicating agents, does inter-agent communication cause outputs to converge? We introduce BOUNDARY_

Conditional Co-Ablation: Recovering Self-Repair Backups in Transformer Circuits

arXiv:2607.01940v1 Announce Type: cross Abstract: Mechanistic interpretability often relies on component-level interventions to discover how a model produces a behavior. This guides attribution, capab

researcharxiv-cs-ai

Distributed Attacks in Persistent-State AI Control

arXiv:2607.02514v1 Announce Type: new Abstract: As AI coding agents become more autonomous, they increasingly ship code iteratively, with the codebase persisting across sessions. This persistence crea

Distributionally Robust Listwise Preference Optimization

arXiv:2607.01715v1 Announce Type: new Abstract: Existing robust preference optimization for language-model alignment mainly studies pairwise supervision and places robustness at the dataset, prompt, o

EO-Agents: A Three-Agent LLM Pipeline for Earth Observation Hypothesis Generation

arXiv:2607.01584v1 Announce Type: new Abstract: Large language models have recently been explored for scientific hypothesis generation, but most prior work relies on unstructured literature and free-f

EvoPolicyGym: Evaluating Autonomous Policy Evolution in Interactive Environments

arXiv:2607.02440v1 Announce Type: new Abstract: Autonomous agents are increasingly expected to improve executable policies through feedback, yet existing evaluations often collapse this process into a

MMIR-TCM: Memory-Integrated Multimodal Inference and Retrieval for TCM Clinical Decision Support

arXiv:2607.01814v1 Announce Type: new Abstract: Traditional Chinese Medicine (TCM) diagnosis, particularly through tongue inspection, faces persistent challenges in subjectivity and reproducibility. T

MultAttnAttrib: Training-Free Multimodal Attribution in Long Document Question Answering

arXiv:2607.01420v1 Announce Type: cross Abstract: As grounded QA systems are increasingly deployed in AI assistants, accurately attributing generated answers to evidence is critical for user trust and

Optimizing RAG Rerankers with LLM Feedback via Reinforcement Learning

arXiv:2604.02091v2 Announce Type: replace-cross Abstract: Rerankers play a pivotal role in refining retrieval results for Retrieval-Augmented Generation. However, current reranking models are typicall

researcharxiv-cs-ai

Safety Targeted Embedding Exploit via Refinement

arXiv:2607.01859v1 Announce Type: new Abstract: Safety training for large language models (LLMs) is conducted predominantly in English, leaving uncertain how well safety mechanisms generalize to low-r

SCAPE: Accurate and Efficient LLM Training with Extreme Sparse Communication

arXiv:2607.01678v1 Announce Type: new Abstract: Communication increasingly dominates the cost of Large Language Model (LLM) pre-training, especially under data-parallel and sharded training schemes, w

The Dual Nature of LLM Persona: Aggregated Tendencies and Frame-Dependent Geometry

arXiv:2607.02368v1 Announce Type: cross Abstract: Evaluations of LLM personas via psychometric questionnaires typically rely on aggregate scores, discarding within-instance correlation structure. We t

researcharxiv-cs-ai

The Eticas AI Risk Taxonomy: Open Infrastructure for Operationalizing AI Audits

arXiv:2607.02201v1 Announce Type: cross Abstract: The rapid deployment of AI systems across high-stakes domains has created urgent demand for standardized evaluation, yet the field remains fragmented

WARP: Weight-Space Analysis for Recovering Training Data Portfolios

arXiv:2607.01686v1 Announce Type: new Abstract: Foundation models are routinely released to the public, yet the data recipes used to train them -- such as domain mixture weights that determine how dif

Benchmarking Frontier LLMs on Arabic Cultural and Sociolinguistic Knowledge: A Cross-Evaluation Framework with Human SME Ground Truth

arXiv:2607.00139v1 Announce Type: new Abstract: The cost of human expert evaluation is a principal bottleneck to deploying language models in specialized, high-stakes domains. This is particularly acu

EPC: A Standardized Protocol for Measuring Evaluator Preference Dynamics in LLM Agent Systems

arXiv:2607.00297v1 Announce Type: cross Abstract: When LLM agents use evaluator feedback to adapt their behavior in closed loops, evaluator biases propagate through the agent's strategy distribution -

GameDevBench: Evaluating Agentic Capabilities Through Game Development

arXiv:2602.11103v2 Announce Type: replace Abstract: Despite rapid progress on coding agents, progress on their multimodal counterparts has lagged behind. A key challenge is the scarcity of evaluation

GRACE-RAG: Governed Retrieval Architecture for Canonical Evidence Synthesis, Enabling Lightweight Deployment in Closed-Domain Institutional Settings

arXiv:2607.00013v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) systems are widely used in institutional question answering settings where responses must be grounded in authorit

QCA: Query- and Content-Aware Keyframe Selection for Long Video Understanding

arXiv:2607.00983v1 Announce Type: new Abstract: Video understanding is often plagued by severe temporal redundancy, where processing dense frame sequences is both semantically inefficient and computat

researcharxiv-cs-cv

Recovering Input Text from Hidden States: Study of Gradient-Based Inversion of Decoder-Only Language Models

arXiv:2607.00852v1 Announce Type: cross Abstract: This work studies the hidden-state inversion problem: recovering the original input token sequence of a decoder-only language model from its last-laye

Representation as a Bottleneck for Mechanistic Interpretability: The Manifestation Unit Protocol

arXiv:2607.00089v1 Announce Type: new Abstract: Mechanistic interpretability has produced a rich inventory of component-level analyses that characterise what neural-network components encode and how t

researcharxiv-cs-lg

Retrieved Images as Visual Thought: Training-Free Multimodal In-Context Learning for the Open-vs-Closed Gap

model-releasesarxiv-cs-cv

arXiv:2607.00606v1 Announce Type: new Abstract: Recent work on Thinking with Images makes vision a dynamic part of reasoning, but does so through generation: the model invokes external tools, synthesi

Safety

Self-Evolving Agents with Anytime-Valid Certificates

arXiv:2607.00871v1 Announce Type: new Abstract: Self-evolving agents violate the assumption behind most learning-theoretic guarantees: the data, evaluator, components, and hypothesis space are produce

safetyarxiv-cs-ai

Steal the Patch Size: Adversarially Manipulate Vision-Language Models

model-releasesarxiv-cs-cv

arXiv:2607.00174v1 Announce Type: new Abstract: We present a black-box model-stealing attack that recovers private vision-tokenizer configurations of deployed vision-language models (VLMs), including

Testing Frontier Large Language Models' Physics Literacy in Parallel Physical Worlds

arXiv:2607.00276v1 Announce Type: cross Abstract: Current large-language-model (LLM) physics benchmarks are usually scored by answer accuracy, which cannot distinguish genuine reasoning from recall of

Toward Cybersecurity-Expert Small Language Models

arXiv:2510.14113v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are transforming everyday applications, yet deployment in cybersecurity lags due to a lack of high-quality, domai

VisReason: A Large-Scale Dataset for Visual Chain-of-Thought Reasoning

model-releasesarxiv-cs-cv

arXiv:2511.17731v2 Announce Type: replace Abstract: Chain-of-Thought (CoT) prompting has proven remarkably effective for eliciting complex reasoning in large language models (LLMs). Yet, its potential

What's Hidden Matters: Identifying Planning-Critical Occluded Agents using Vision-Language Models

arXiv:2607.00283v1 Announce Type: cross Abstract: Autonomous vehicles must safely navigate complex environments where planning-critical agents may be hidden from view. Current approaches often treat a

An Empirical Study of Security Calibration in Large Language Models for Code

arXiv:2606.31159v1 Announce Type: cross Abstract: Large Language Models (LLMs) are rapidly transforming software development, yet their use in security-critical contexts raises a key question: do mode

AutoTrainess: Teaching Language Models to Improve Language Models Autonomously

arXiv:2606.31551v1 Announce Type: new Abstract: Training language models (LMs) remains a highly human-intensive process, even as frontier language model agents become increasingly capable at software

AxDafny: Agentic Verified Code Generation in Dafny

arXiv:2606.32007v1 Announce Type: new Abstract: We study agentic code generation in Dafny, where a model must generate both executable code and the proof artifacts for verification. We present AxDafny

Benchmarking Large Language Models on Floating-Point Error Classification

arXiv:2606.31308v1 Announce Type: new Abstract: This paper investigates the capability of Large Language Models (LLMs) to detect and classify floating-point errors statically in software code. We intr

Calibration, Not Compilation: Detecting and Repairing Misspecified Probabilistic Programs Written by Language Models

arXiv:2606.31630v1 Announce Type: new Abstract: Language models increasingly write probabilistic programs (in NumPyro, Stan, or Pyro), but a program that compiles, runs, and passes every unit test can

Generative Skill Composition for LLM Agents

arXiv:2606.32025v1 Announce Type: new Abstract: Recent LLM agents benefit from skills for solving complex tasks. Skills encapsulate modular packages of procedural knowledge and instructions for perfor

LLM-as-a-judge validity in physics assessment depends more on the task than the model

arXiv:2603.14732v2 Announce Type: replace-cross Abstract: As large language models (LLMs) are increasingly considered for automated assessment and feedback, understanding when LLM marking is valid is

Modality-Driven Search with Holistic Trace Judging for ARC-AGI-2

arXiv:2606.31543v1 Announce Type: new Abstract: Large language models can produce fluent, internally coherent reasoning traces for abstract reasoning tasks while still being confidently wrong - making

Seeing Through Multiple Views: Parameter-Efficient Fine-Tuning via Selective Neurons for Consistent Radiology Report Generation

arXiv:2606.31099v1 Announce Type: cross Abstract: Recent years have seen substantial advances in radiology report generation (RRG), yet existing approaches predominantly adopt direct feature fusion wh

Surrogate Fidelity: When Can Open LLMs Explain Closed Ones?

arXiv:2606.32008v1 Announce Type: new Abstract: Mechanistic interpretability (MI) requires full access to model internals, yet the APIs for most widely deployed language models at best expose log-prob

Understanding and Evaluating Claw-like Agent Security Through a Computer-Systems Lens

arXiv:2606.30755v1 Announce Type: cross Abstract: Claw-like AI agents (e.g., OpenClaw) are always-on processes with persistent access to credentials, files, tools, and external services. They take on

Local Ai

When transformers learn 'impossible' languages, what do they learn?

arXiv:2606.30815v1 Announce Type: cross Abstract: Recent work suggests that transformer language models show a bias towards human languages over unnatural ('impossible') languages argued to be unacqui

local-aiarxiv-cs-ai

Wisdom Of The (AI) Crowd: Investigating Artificial Swarm Intelligence In Large Language Models

arXiv:2606.31404v1 Announce Type: new Abstract: Human swarm intelligence demonstrates remarkable collective accuracy but faces scalability constraints in cost, coordination, and time. We investigate w

An AI agent for treatment reasoning over a biomedical tool universe

arXiv:2606.28692v1 Announce Type: new Abstract: Treatment reasoning underpins every therapeutic decision, integrating disease context, comorbidities, medications, contraindications, and evolving biome