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DeepEval+15% downloads · 769.3k/wk+14.7%
Ragas+14% downloads · 319.9k/wk+13.6%

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Open problems in Agents

Long-horizon task execution and evidence accumulation3 papers raise it · 2 address it
Agents struggle to sustain multi-step reasoning, adapt strategies, and turn intermediate results into final deliverables in real-world tasks.
- Scaling Agentic Data for Long-Horizon Terminal Intelligenceraises · addresses
- FDE-Gym: The Last Mile of AI Deliveryraises · addresses
- AgentSWE: Can Coding Agents Build the Agent You Actually Want?raises
Realistic evaluation of agentic learning from experience1 paper raises it · 1 addresses it
Existing benchmarks often test reasoning with pre-existing knowledge rather than learning from novel, unfamiliar environments.
- Learn2Play Bench: How Well Do LLM Agents Learn from Experience in Unfamiliar Environments?raises · addresses
Faithful agent development from natural-language requirements1 paper raises it · 1 addresses it
Coding agents often fail to deliver agents that truly meet the specified requirements, not just appear to.
- AgentSWE: Can Coding Agents Build the Agent You Actually Want?raises · addresses
Detecting when past context matters in long conversations1 paper raises it · 0 address it
AI systems cannot reliably identify when historical context is relevant for current interactions in long-term companionship.
- RealCompanion: Benchmarking Human Understanding from Reasoning over Longitudinal Real-World Conversationsraises
Scalable and realistic GUI agent training and evaluation1 paper raises it · 2 address it
High cost and scarcity of realistic interactive environments limit progress in GUI agent training and evaluation.
- MAI-Gym: An Open All-in-One Platform for GUI Agent Evaluation and RL Training across Mobile and Computer Environmentsraises · addresses
- OpenGCUA: Toward a Fully Open Stack for Generalist Computer Useaddresses

Named by an AI from these papers' abstracts.

Open problems

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Asymptote LabsSeed · Oct 20$5.5M
GreptileSeries B+ · Oct 19$50M
VesslaPre-seed · Oct 19$3.3M
SupaHumanSeries A · Oct 19$1.5M
BoxLiteSeed · Oct 19undisclosed

Recently funded

Topics in this field

coding agents · code generation · coding agent286 papers87 this week
skills · tasks · ai306 papers65 this week
tool using agents · agent systems · multi agent209 papers53 this week
recursive self improvement · harness evolution · adaptive260 papers52 this week
ai agents · indirect prompt injection · adversarial313 papers48 this week
vision language · safety evaluations · safety alignment115 papers38 this week

Topics in this field

Active contributors · 90 days

Active contributors

Activity · 12 weeks

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arXiv cs.CRCodeOct 13▲ 4
CyberArena: Benchmarking AI Agents in Live Symmetric Attack-and-Defense Combat
Bangya Liu, Jianhong Tu, Yueyang Pan, Hanhong Zhao, Zhe Ye +16
AI agents are demonstrating increasingly strong offensive and defensive cybersecurity capabilities in the real world. Current benchmarks mostly evaluate agents in isolation against static targets. Real cyber operations, however, are dynamic and coupled: security postures constantly change as attackers and defenders adapt to each other in real time. Static benchmarks quickly saturate and miss an agent's ability to attack and defend simultaneously under live pressure. To address this gap, we present CyberArena, a dynamic, symmetric adversarial benchmark built around multi-round attack-and-defense combat. In isolated sandboxes, two agents compete under identical rules, acting concurrently as both attackers and defenders over continuously changing services. This converts static targets into evolving, semi-open challenges, directly testing an agent's speed and precision in discovering, exploiting, and patching vulnerabilities. To sustain the benchmark's longevity, an automated agentic pipeline continuously generates, reviews, and validates new environments. Furthermore, because pairwise contests are notoriously hard to quantify, we introduce a dynamical model that maps match dynamics…
PDF arXiv Code
arXiv cs.AICodeOct 13▲ 201
Self-Evolving Agent Harness
Chuyang Wei, Haoxiang Guan, Minghang Zhu, Kexin Zhou, Jiyan He +3
Harness self-evolution revises an agent's software scaffolding, including prompts, memory, workflows, and executable components, while keeping model weights fixed. We formalize a feedback-driven process in which accepted revisions persist across runs. The framework separates update targets from proposal and admission mechanisms. It distinguishes changes to interpreted state from revisions to tools and code, repository-level control logic, and plugin composition. We characterize search through evolutionary optimization and examine its reliability through continual PAC-learning. Under explicit assumptions, the analysis yields an expected generalization bound, a retention bound conditional on historical regression budgets, and a high-probability admission rule using fresh, independent validation data. We examine how persistent harness revisions affect knowledge sharing and organizational workflows, with implications for scientific reliability, physical safety, and institutional accountability. Cases from prior work illustrate model--harness co-design, distributed execution, and plugin reuse. Reliable deployment requires continual validation as task distributions and evaluators…
PDF arXiv Code
arXiv cs.AICodeOct 13▲ 30
ReTerm: Recovery Training for Self-Improving Terminal Agents
Wei Xie, Kaituo Feng, Shiming Fu, Yong Liu, Xiangyu Yue +1
Terminal agents have shown strong abilities in complex coding tasks. However, they often struggle to recover from errors during long-running execution (e.g., command errors, faulty implementations), eventually leading to task failures. To this end, we present ReTerm, a self-improving terminal agent trained by learning how to recover from its own failed terminal tasks. To support its training, we construct a customized data pipeline and produce ReTerm-10K, a high-quality dataset that provides effective recovery demonstrations for SFT cold start. Furthermore, we propose ReTerm-GRPO, an online RL algorithm that forks from the agent's own failure states to generate multiple independent self-recovery attempts. By learning from whether these attempts successfully complete the task, the agent self-improves through its own recovery experience. Built on Qwen3.5-9B, our ReTerm-9B achieves 34.8% Pass@1 on Terminal-Bench 2.0, improving over the base model by 14.6 points. Further analysis shows that our method achieves consistently higher recovery rates across diverse error categories compared with standard GRPO training. All code, data and model are released at…
PDF arXiv Code
arXiv cs.SECodeOct 13▲ 18
SWE-MISBEHAVIOR: Benchmarking and Mitigating Boundary Violations in Software Coding Agents
Do Xuan Trong, Duc Anh Nguyen, Do Xuan Long, Ngoc-Hai Nguyen, Tongyao Zhu +10
We present SWE-MISBEHAVIOR, the first taxonomy and evaluation framework for diagnosing boundary-violating misbehaviors in coding agents. It contains 7.4K human-in-the-loop synthesized samples spanning three layers of agent action space: (i) Code Generation, (ii) File Management, and (iii) System Interaction. Across these categories, SWE-MISBEHAVIOR covers 11 distinct misbehaviors with a fully automated evaluation protocol, including coding-action violations, Create/Read/Update/Delete (CRUD) file misbehaviors, system integrity and abuse violations, and task-goal misalignment. Experiments on state-of-the-art open-source coding agents (e.g., OpenHands, smolagents) reveal substantial misaligned behaviors, including dependency misinterpretation (up to 98.5% of cases), unintended modification of user inputs (50.5%), and silent compromise of environment integrity (53%). Motivated by these findings, we further propose CODE-KIT, a lightweight adaptive framework that combines misbehavior-aware guidelines with novel tools to help agents avoid such failures. CODE-KIT reduces misbehaviors by 52% for smolagents and 34% for OpenHands, while simultaneously improving task completion performance.
PDF arXiv Code
arXiv cs.CVCodeOct 13▲ 21
APT: Feedback-Driven Agentic Post-Training for Text-to-Image Generation
Sirui Zhang, Fangyun Wei, Dong Chen, Jinjing Zhao, Yitong Wang +3
Post-training is crucial for improving text-to-image models, yet existing pipelines typically rely on manually curated training data and fixed training distributions. We introduce APT (Agentic Post-Training), a fully automated framework that adaptively post-trains a base model according to its evolving capability profile. APT coordinates three agents to construct evaluation sets from user requirements, diagnose model-specific strengths and failure modes, and generate reinforcement learning data with an adaptive prompt distribution that emphasizes underperforming capabilities. By repeatedly evaluating, diagnosing, and updating the generator, APT lets the current model's behavior shape its next training tasks, forming a feedback loop inspired by recursive self-improvement. For general-purpose settings without user-specified evaluation scenarios, we further develop an evaluation harness that enables broad and diverse evaluation set generation. Experiments across multiple base models, benchmarks, and generation architectures demonstrate that APT is model-agnostic, benchmark-agnostic, architecture-agnostic, and fully automated.
PDF arXiv Code
arXiv cs.AICodeOct 13▲ 85
WorkRover: Scaling Long-Horizon Cowork Agent Training Via Recursive Environment Evolution
Hang He, Linting Wang, Guanming Liu, Bo Mao, Jinxiu Liu +12
Scaling interactive environments is important for training long-horizon agents through reinforcement learning. These environments often require further testing and refinement after initial generation, and correcting defects in a task may also require updating its work context and verifier. However, existing construction pipelines offer limited support for such iterative refinement while keeping these components aligned. We introduce WorkRover, a framework for training long-horizon cowork agents through recursive environment evolution. Its harness-driven synthesis enables construction agents to jointly build and validate semantically aligned work contexts, executable tasks, and evidence-grounded verifiers, adapting their actions to workspace state and execution feedback rather than following a fixed construction workflow. To support environment evolution, we introduce Factorial Compatibility Replay (FCR), which uses rollout feedback to evaluate combinations of component revisions through dependency-closed replay and selects the smallest validated repair among tested candidates while preserving task requirements and evaluation constraints. To provide finer-grained training signals,…
PDF arXiv Code

Papers

Top tools here

Coming with more sources: stars history

All tools

Agno4 releases this week
FastMCP6 releases this week
n8n+25% downloads · 148.3k/wk+25.0%
DeepEval+15% downloads · 769.3k/wk+14.7%
Ragas+14% downloads · 319.9k/wk+13.6%

Topics in this field

coding agents · code generation · coding agent286 papers87 this week
skills · tasks · ai306 papers65 this week
tool using agents · agent systems · multi agent209 papers53 this week
recursive self improvement · harness evolution · adaptive260 papers52 this week
ai agents · indirect prompt injection · adversarial313 papers48 this week
vision language · safety evaluations · safety alignment115 papers38 this week

Recently funded

Market

Asymptote LabsSeed · Oct 20$5.5M
GreptileSeries B+ · Oct 19$50M
VesslaPre-seed · Oct 19$3.3M
SupaHumanSeries A · Oct 19$1.5M
BoxLiteSeed · Oct 19undisclosed

Active contributors · 90 days

Recent papers

All in Research →

arXiv cs.CRCodeOct 13▲ 4
CyberArena: Benchmarking AI Agents in Live Symmetric Attack-and-Defense Combat
Bangya Liu, Jianhong Tu, Yueyang Pan, Hanhong Zhao, Zhe Ye +16
AI agents are demonstrating increasingly strong offensive and defensive cybersecurity capabilities in the real world. Current benchmarks mostly evaluate agents in isolation against static targets. Real cyber operations, however, are dynamic and coupled: security postures constantly change as attackers and defenders adapt to each other in real time. Static benchmarks quickly saturate and miss an agent's ability to attack and defend simultaneously under live pressure. To address this gap, we present CyberArena, a dynamic, symmetric adversarial benchmark built around multi-round attack-and-defense combat. In isolated sandboxes, two agents compete under identical rules, acting concurrently as both attackers and defenders over continuously changing services. This converts static targets into evolving, semi-open challenges, directly testing an agent's speed and precision in discovering, exploiting, and patching vulnerabilities. To sustain the benchmark's longevity, an automated agentic pipeline continuously generates, reviews, and validates new environments. Furthermore, because pairwise contests are notoriously hard to quantify, we introduce a dynamical model that maps match dynamics…
PDF arXiv Code
arXiv cs.AICodeOct 13▲ 201
Self-Evolving Agent Harness
Chuyang Wei, Haoxiang Guan, Minghang Zhu, Kexin Zhou, Jiyan He +3
Harness self-evolution revises an agent's software scaffolding, including prompts, memory, workflows, and executable components, while keeping model weights fixed. We formalize a feedback-driven process in which accepted revisions persist across runs. The framework separates update targets from proposal and admission mechanisms. It distinguishes changes to interpreted state from revisions to tools and code, repository-level control logic, and plugin composition. We characterize search through evolutionary optimization and examine its reliability through continual PAC-learning. Under explicit assumptions, the analysis yields an expected generalization bound, a retention bound conditional on historical regression budgets, and a high-probability admission rule using fresh, independent validation data. We examine how persistent harness revisions affect knowledge sharing and organizational workflows, with implications for scientific reliability, physical safety, and institutional accountability. Cases from prior work illustrate model--harness co-design, distributed execution, and plugin reuse. Reliable deployment requires continual validation as task distributions and evaluators…
PDF arXiv Code
arXiv cs.AICodeOct 13▲ 30
ReTerm: Recovery Training for Self-Improving Terminal Agents
Wei Xie, Kaituo Feng, Shiming Fu, Yong Liu, Xiangyu Yue +1
Terminal agents have shown strong abilities in complex coding tasks. However, they often struggle to recover from errors during long-running execution (e.g., command errors, faulty implementations), eventually leading to task failures. To this end, we present ReTerm, a self-improving terminal agent trained by learning how to recover from its own failed terminal tasks. To support its training, we construct a customized data pipeline and produce ReTerm-10K, a high-quality dataset that provides effective recovery demonstrations for SFT cold start. Furthermore, we propose ReTerm-GRPO, an online RL algorithm that forks from the agent's own failure states to generate multiple independent self-recovery attempts. By learning from whether these attempts successfully complete the task, the agent self-improves through its own recovery experience. Built on Qwen3.5-9B, our ReTerm-9B achieves 34.8% Pass@1 on Terminal-Bench 2.0, improving over the base model by 14.6 points. Further analysis shows that our method achieves consistently higher recovery rates across diverse error categories compared with standard GRPO training. All code, data and model are released at…
PDF arXiv Code
arXiv cs.SECodeOct 13▲ 18
SWE-MISBEHAVIOR: Benchmarking and Mitigating Boundary Violations in Software Coding Agents
Do Xuan Trong, Duc Anh Nguyen, Do Xuan Long, Ngoc-Hai Nguyen, Tongyao Zhu +10
We present SWE-MISBEHAVIOR, the first taxonomy and evaluation framework for diagnosing boundary-violating misbehaviors in coding agents. It contains 7.4K human-in-the-loop synthesized samples spanning three layers of agent action space: (i) Code Generation, (ii) File Management, and (iii) System Interaction. Across these categories, SWE-MISBEHAVIOR covers 11 distinct misbehaviors with a fully automated evaluation protocol, including coding-action violations, Create/Read/Update/Delete (CRUD) file misbehaviors, system integrity and abuse violations, and task-goal misalignment. Experiments on state-of-the-art open-source coding agents (e.g., OpenHands, smolagents) reveal substantial misaligned behaviors, including dependency misinterpretation (up to 98.5% of cases), unintended modification of user inputs (50.5%), and silent compromise of environment integrity (53%). Motivated by these findings, we further propose CODE-KIT, a lightweight adaptive framework that combines misbehavior-aware guidelines with novel tools to help agents avoid such failures. CODE-KIT reduces misbehaviors by 52% for smolagents and 34% for OpenHands, while simultaneously improving task completion performance.
PDF arXiv Code
arXiv cs.CVCodeOct 13▲ 21
APT: Feedback-Driven Agentic Post-Training for Text-to-Image Generation
Sirui Zhang, Fangyun Wei, Dong Chen, Jinjing Zhao, Yitong Wang +3
Post-training is crucial for improving text-to-image models, yet existing pipelines typically rely on manually curated training data and fixed training distributions. We introduce APT (Agentic Post-Training), a fully automated framework that adaptively post-trains a base model according to its evolving capability profile. APT coordinates three agents to construct evaluation sets from user requirements, diagnose model-specific strengths and failure modes, and generate reinforcement learning data with an adaptive prompt distribution that emphasizes underperforming capabilities. By repeatedly evaluating, diagnosing, and updating the generator, APT lets the current model's behavior shape its next training tasks, forming a feedback loop inspired by recursive self-improvement. For general-purpose settings without user-specified evaluation scenarios, we further develop an evaluation harness that enables broad and diverse evaluation set generation. Experiments across multiple base models, benchmarks, and generation architectures demonstrate that APT is model-agnostic, benchmark-agnostic, architecture-agnostic, and fully automated.
PDF arXiv Code
arXiv cs.AICodeOct 13▲ 85
WorkRover: Scaling Long-Horizon Cowork Agent Training Via Recursive Environment Evolution
Hang He, Linting Wang, Guanming Liu, Bo Mao, Jinxiu Liu +12
Scaling interactive environments is important for training long-horizon agents through reinforcement learning. These environments often require further testing and refinement after initial generation, and correcting defects in a task may also require updating its work context and verifier. However, existing construction pipelines offer limited support for such iterative refinement while keeping these components aligned. We introduce WorkRover, a framework for training long-horizon cowork agents through recursive environment evolution. Its harness-driven synthesis enables construction agents to jointly build and validate semantically aligned work contexts, executable tasks, and evidence-grounded verifiers, adapting their actions to workspace state and execution feedback rather than following a fixed construction workflow. To support environment evolution, we introduce Factorial Compatibility Replay (FCR), which uses rollout feedback to evaluate combinations of component revisions through dependency-closed replay and selects the smallest validated repair among tested candidates while preserving task requirements and evaluation constraints. To provide finer-grained training signals,…
PDF arXiv Code

Activity · 12 weeks

Coming with more sources: repos

Fields · AI · updated 1h ago

Agents

LLM agents: planning, tool use, multi-agent systems, computer/web agents and agent benchmarks.

561papers this week+8.9% vs last week

All papers · 2.5k With code · 112

Newest Most relevant

2.4k papers this month

Open problems in Agents

Long-horizon task execution and evidence accumulation3 papers raise it · 2 address it
Agents struggle to sustain multi-step reasoning, adapt strategies, and turn intermediate results into final deliverables in real-world tasks.
- Scaling Agentic Data for Long-Horizon Terminal Intelligenceraises · addresses
- FDE-Gym: The Last Mile of AI Deliveryraises · addresses
- AgentSWE: Can Coding Agents Build the Agent You Actually Want?raises
Realistic evaluation of agentic learning from experience1 paper raises it · 1 addresses it
Existing benchmarks often test reasoning with pre-existing knowledge rather than learning from novel, unfamiliar environments.
- Learn2Play Bench: How Well Do LLM Agents Learn from Experience in Unfamiliar Environments?raises · addresses
Faithful agent development from natural-language requirements1 paper raises it · 1 addresses it
Coding agents often fail to deliver agents that truly meet the specified requirements, not just appear to.
- AgentSWE: Can Coding Agents Build the Agent You Actually Want?raises · addresses
Detecting when past context matters in long conversations1 paper raises it · 0 address it
AI systems cannot reliably identify when historical context is relevant for current interactions in long-term companionship.
- RealCompanion: Benchmarking Human Understanding from Reasoning over Longitudinal Real-World Conversationsraises
Scalable and realistic GUI agent training and evaluation1 paper raises it · 2 address it
High cost and scarcity of realistic interactive environments limit progress in GUI agent training and evaluation.
- MAI-Gym: An Open All-in-One Platform for GUI Agent Evaluation and RL Training across Mobile and Computer Environmentsraises · addresses
- OpenGCUA: Toward a Fully Open Stack for Generalist Computer Useaddresses

Named by an AI from these papers' abstracts.