Research category
Agent systems, tool use, memory, AI research workflows, and reusable skill ecosystems.
1 paper
This paper proposes a failure-driven inference-time self-improvement loop for computer-use agents that turns failed trajectories into LLM-diagnosed strategy and code patches, rather than discarding them. On OSWorld, it upgrades OpenCUA-72B from 42.3% to 48.9% without additional training and only modest inference overhead.
3 papers
This paper empirically studies how retrieval strategy, agent harness design, and tool-result delivery interact in agentic search. Across LongMemEval experiments with Chronos and provider CLI harnesses, grep often outperforms vector retrieval, while overall performance remains strongly shaped by the harness and tool-calling style.
This paper introduces Direct Corpus Interaction (DCI), a retrieval paradigm in which agents search raw corpora with general-purpose terminal tools such as grep, file reads, and lightweight scripts instead of fixed top-k retrievers. DCI requires no embedding model, vector index, or retrieval API, and substantially outperforms sparse, dense, and reranking baselines on IR benchmarks and end-to-end agentic search tasks.
This paper identifies brittleness in current multimodal tool-using reasoning under simple image orientation changes and corruptions, and proposes CodeVision, a code-as-tool framework that lets models invoke arbitrary image operations through generated code. It combines SFT and RL with dense process rewards to improve multi-tool reasoning, execution efficiency, and error recovery on thinking-with-images tasks.
1 paper
Tongyi DeepResearch is a 30.5B-parameter agentic language model with 3.3B parameters activated per token, built for long-horizon information-seeking tasks through agentic mid-training and post-training. A fully automatic data synthesis pipeline and stage-specific environments enable scalable, stable interactions across training stages.
2 papers
Data agents use LLMs and tools to automate data management, preparation, and analysis, but inconsistent definitions obscure capability and accountability boundaries. This tutorial introduces an L0-L5 autonomy taxonomy and lifecycle-driven survey, mapping current systems and outlining a roadmap toward proactive and generative data agents.
Clio is a privacy-preserving platform that uses AI assistants to extract, cluster, and summarize aggregate patterns from millions of conversations without requiring human reviewers to inspect raw data. Its evaluations and deployment on one million Claude.ai conversations show how large-scale usage analysis can reveal real-world applications and emerging safety risks while protecting user privacy.
6 papers
This survey defines agentic artifact creation as stateful construction in which an AI system materially builds or revises a deliverable, with intermediate observations redirecting later work. It reviews 230 systems and 29 benchmarks across six artifact families, analyzes construction and evaluation challenges, and proposes principles for explicit responsibility, targeted repair, and post-change revalidation.
AutoResearch is a two-stage multi-agent system that links research-idea generation with evidence-grounded execution, using cross-model review, experiment decomposition, and independent audits. Across cross-modal retrieval, systems optimization, and benchmark-driven machine learning, it turns candidate ideas into measurable progress while detecting and correcting unreliable experimental results.
Spark-to-Paper implements end-to-end research paper generation as thirteen composable skills inside an existing coding assistant, covering literature retrieval, experiment planning and execution, evidence-guided revision, and editable figure production. It combines deterministic integrity checks with self-critique and bounded recovery from self-refutation loops to keep long-horizon research workflows grounded in measured evidence.
This survey analyzes AI-assisted research across creation, writing, validation, and dissemination, showing where automation is reliable and where autonomy still fails on novelty, experiments, and scientific judgment. It provides a lifecycle taxonomy, benchmark suite, tool inventory, design principles, and practitioner playbook for human-governed AI research workflows.
This paper proposes Crafter, a multi-agent harness for generating publication-style scientific figures across multiple figure types and input conditions, and CraftEditor for converting raster outputs into editable SVGs. It also introduces CraftBench, a human-annotated benchmark for scientific figure generation, and shows gains over standalone generators and agentic baselines.
This paper introduces AIRA_2, an AI research agent architecture that addresses limited experiment throughput, noisy validation-based selection, and static single-turn operators. It combines asynchronous multi-GPU workers, Hidden Consistent Evaluation, and interactive ReAct agents to improve long-horizon research task performance.
4 papers
SkillEvo evolves agent skills using multi-turn user simulation to continuously generate feedback that reveals layered interaction failures. An independent governance layer repairs factual degradation and structural bloat, outperforming self-reflection and single-turn-QA-driven evolution across production cloud-service skills.
SkillsVote addresses noisy and hard-to-govern agent trajectories by treating Agent Skills as reusable experience artifacts with collection, recommendation, attribution, and evolution controls. It profiles large-scale open-source skill corpora, recommends structured skill context before execution, and admits only evidence-gated successful discoveries to improve frozen agents without model updates.
Ctx2Skill addresses context learning for long, dense contexts where manual skill annotation is costly and automated skill construction lacks external feedback. It uses a multi-agent self-play loop with Cross-time Replay to autonomously discover, refine, and select reusable natural-language skills that improve solving rates across language models.
This paper presents SkillReducer, a two-stage optimization framework that compresses LLM agent skills (pre-packaged instruction sets) by 48% for descriptions and 39% for body while improving functional quality by 2.8%, reducing token costs and attention dilution in agent contexts.
11 papers
LongHorizon-Harness reframes long-horizon execution as explicit task-state management, updating external state only with facts independently verified from the environment. Its Manage-Execute-Audit loop separates planning, fresh-context execution, and read-only verification, improving reliability across computer-use and terminal benchmarks.
OneDayAgent turns open-ended long-horizon requests into a managed execution process that combines bounded subtask decomposition, execution memory under context pressure, and global verification with targeted repair across heterogeneous tools. On 104 AgentIF-OneDay tasks, it scores 0.821 with GLM-5.2 and runs across five backend LLMs from three model families without backend-specific tuning.
This survey defines self-evolving coding agents and organizes the field through an object-centered taxonomy of evolving frameworks, memory, skills, tools, models, and collaboration structures, complemented by when evolution occurs and which software-specific evidence drives it. It also identifies executable feedback, repository context, and coding trajectories as distinctive foundations for evolution while outlining challenges in reliability, safety, cost, maintainability, and generalization.
Prime Agent is an open-source harness for long-horizon evaluation and coding workflows that combines a persistent IPython REPL, durable histories and reusable agent assets, and recursively coordinated subagents. It standardizes execution, recovery, verification, and resource accounting while letting models construct their own strategies, substantially improving performance across reasoning, coding, and autonomous-task benchmarks.
Apodex 1.1 develops sustained, verifiable progress on complex real-world tasks through Environment Scaling and Agentic Coordination Scaling, combining executable file, search, and code environments with stateful multi-agent decomposition, asynchronous coordination, and replanning. Its shared execution harness and AgentOS turn trajectories and coordination traces into reliable behavior, while the 35B-parameter Apodex 1.1 Mini provides a locally deployable form of this working capability.
This paper introduces Harness Handbook, a behavior-centric representation that combines static analysis with LLM-assisted structuring to map distributed agent-harness behaviors to their source code. Its Behavior-Guided Progressive Disclosure method improves behavior localization and edit planning while reducing planner token usage.
This paper introduces LLM-as-a-Verifier, a training-free framework that derives continuous verification scores from scoring-token probability distributions and scales verification through finer score granularity, repeated evaluation, and criteria decomposition. It provides accurate, fine-grained feedback for selecting and tracking agent solutions across coding, robotics, and medicine.
This survey formalizes long-horizon agency as a harness-coupled decision process and presents a unified taxonomy spanning externalized harness engineering and internalized model optimization. It organizes the field's evolution, applications, benchmarks, and open problems through six connected perspectives.
This paper proposes Nurture-First Development, a paradigm for growing domain-expert agents through structured conversational interaction rather than fixed code-first or prompt-first construction. It formalizes a Knowledge Crystallization Cycle, Three-Layer Cognitive Architecture, Dual-Workspace Pattern, and Spiral Development Model for continuously converting tacit practitioner knowledge into reusable agent assets.
This paper proposes EvoControl, a controlled self-evolution framework for algorithmic code optimization that balances correctness with exploration across generate-verify-refine cycles. It uses staged self-evolution, genetic-style population search, and evolutionary memory to improve code quality on challenging algorithmic benchmarks.
Darwin Gödel Machine (DGM) is a self-improving coding-agent system that repeatedly rewrites its own code, evaluates each change on coding benchmarks, and explores an expanding archive of agent variants. It raises performance from 20.0% to 50.0% on SWE-bench and from 14.2% to 30.7% on Polyglot, outperforming ablations without self-improvement or open-ended exploration.
5 papers
SWE-Bench ProMax is an expert-curated benchmark of 170 large-scale code-refactoring tasks drawn from real commits across seven programming languages, with rewritten specifications and manually reviewed tests. Its cross-file tasks average 11.4 modified files and 261.6 changed lines, while the best evaluated frontier model resolves only 41.2%.
HarnessEval-W is an agentified pipeline for evaluating world-model rollouts: a parent agent decomposes each case into measurable subproblems, delegates them to tool-equipped specialists, validates their evidence, and produces an auditable evidence tree instead of only a scalar score. Across 330 cases and 18 world models, its judgments closely align with human preferences while providing fine-grained diagnoses.
LoopArena benchmarks how well a Controller model guides a separate, fixed coding-agent Worker through long-running software tasks using structured evidence and loop contracts. Its three evaluation settings range from next-step contract selection to full-task control; the best observed full-task strict success rate is 24.69%, while the lower-cost condensed setting reduces estimated inference cost by 64.4% on average.
AutoLab introduces a benchmark for ultra-long-horizon closed-loop optimization across system optimization, puzzles, model development, and CUDA kernels, with 36 expert-curated tasks that require agents to iteratively edit, run, measure, and improve real artifacts under strict budgets. Evaluations of 17 frontier models show that persistence and empirical feedback, rather than initial attempt quality alone, dominate success, motivating more time-aware and iterative autonomous-agent research.
This survey presents rubrics as explicit, structured criteria that turn holistic quality judgments into verifiable and actionable standards for LLM evaluation and training. It organizes rubric construction, optimization, reliability, and benchmarks, tracing their roles from evaluation through dense process-level feedback to self-improvement.
7 papers
MemTrapBench evaluates memory-induced cognitive traps in which faithful, relevant retrieved memories distort LLM reasoning or beliefs, covering Reasoning Fixation and Belief Distortion. Across memory frameworks, it finds performance below a no-memory baseline and introduces AdaptiveMem, an inference-time prompting method that mitigates these failures while preserving standard memory-benchmark performance.
Agent Memory Distillation is a training-free framework that transfers successful teacher-agent experience to 4B-8B student agents through complementary Workflow, Subtask, and Function memories, using proactive injection for planning and reactive retrieval for tool errors. Across four student models, it improves average accuracy over zero-shot by 27.2, 11.2, and 3.4 percentage points on AppWorld, BFCL V3, and ToolSandbox, respectively.
This paper presents ABot-AgentOS, a deliberative runtime layer above robot controllers that coordinates planning, isolated skill execution, verification, edge-cloud collaboration, and persistent multimodal graph memory. It also introduces EmbodiedWorldBench and a leakage-resistant self-evolution loop that turns diagnosed memory failures into gated runtime improvements for long-horizon embodied tasks.
This paper presents a framework that extracts actionable learnings from LLM agent execution trajectories and retrieves them as contextual memory for future tasks. It combines trajectory intelligence extraction, decision attribution, contextual learning generation, and adaptive memory retrieval to improve AppWorld task completion, especially on complex scenarios.
This paper introduces BEAM, a benchmark of long, coherent conversations and probing questions for evaluating long-term memory in LLMs, and proposes LIGHT, a memory framework with episodic memory, working memory, and a scratchpad. Together, they expose long-context memory limitations and improve performance on long-horizon conversational reasoning tasks.
This paper introduces MLP Memory, a lightweight parametric module that learns to internalize retrieval patterns by pretraining an MLP to imitate a kNN retriever's behavior, bridging the gap between RAG and fine-tuning approaches.
This paper introduces MemAgent, a multi-conversation RL-based memory agent that addresses the challenge of handling infinitely long documents with linear complexity without performance degradation during extrapolation.
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Code
Project
Hugging Face
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