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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%

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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

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

Recently funded

Topics in this field

coding agents · code generation · coding agent286 papers74 this week
skills · tasks · ai308 papers57 this week
ai agents · indirect prompt injection · adversarial314 papers42 this week
tool using agents · agent systems · multi agent209 papers42 this week
recursive self improvement · harness evolution · adaptive260 papers41 this week
vision language · safety evaluations · safety alignment115 papers33 this week

Topics in this field

Active contributors · 90 days

Active contributors

Activity · 12 weeks

Activity

Recent papers

All papers in Research

arXiv cs.AIOct 16
Adaptation of Computer Use Agents to New Software with User Manuals
Chulhoon Yoon, Kyungri Park, Jinyeong Yun, Daeyun Jeong, Sukmin Yun +1
Recent advances in multimodal foundation models have significantly improved the ability to operate real-world software through graphical user interfaces (GUIs) using visual observations and natural language instructions. However, maintaining strong performance on unseen applications remains challenging, as practical tasks often require long-horizon procedural knowledge and application-specific operation patterns. In addition, constructing executable tasks and collecting corresponding trajectories for training still heavily rely on human-annotated data, incurring substantial cost. In this paper, we propose MAGE (Manual-augmented computer use AGent Execution and learning), a framework that leverages user manuals as a scalable source of procedural knowledge to improve both inference and training. During execution, MAGE retrieves task-relevant manual content and uses a planner to generate explicit procedural guidance for CUAs. To further account for the discrepancies between manuals and actual GUI environments, such as version differences or user settings, MAGE also adapts the agent policy through environment interaction. During training, MAGE converts manuals into executable…
PDF arXiv
arXiv cs.HCOct 16
PersonaCUABench: Benchmarking Computer-Use Agents Serving Diverse Personas
Haoming Li, Defu Cao, Xu Hu, Yingtao Luo, Yiyang Wang +21
Computer-use agents (CUAs) are commonly evaluated against fixed task specifications, but users may omit requirements, add constraints, or require approval before consequential actions. Completing the benchmark instruction does not guarantee serving the person who asked. We introduce PersonaCUABench, which holds 74 tasks across 11 domains fixed and varies only who asks. A persona agent, playing one of 44 personas in four groups, rewrites each instruction in that user's own words and answers the CUA's questions, while the CUA sees only the dialogue, never the profile or the original instruction. Across nine backbones (3,330 runs), mean task completion falls by 36% relative to the original requests. The losses have two sources: in 59.9% of cells that lose points, the agent engages with what the user asked for and still gets it wrong, and 11.2% are stops the user requested that the checker scores as failures. Even when a persona request is fully completed, the user gives the top satisfaction rating in only 62.7% of cells, against 84.1% for the original requests. CUAs should therefore be evaluated not only on task completion but also on whether they meet the requirements and…
PDF arXiv
arXiv cs.AIOct 16
From Approval to Abuse: Privilege Escalation Attacks on Harness-Protected Coding Agents
Xianglin Yang, Yechao Zhang, Jiahao Liu, Yulin Chen, Yuxin Cao +2
Coding agents use execution harnesses to constrain the consequences of prompt injection by enforcing permission boundaries. These systems deterministically intercept any out-of-sandbox action and prompt the user for approval. To reduce approval fatigue, users can approve allow-rules proposed by the agent, after which any future action matching the rule is allowed without a prompt. In this work, we show that this mechanism introduces a new risk: untrusted context can influence the permissions presented for approval, with consequences extending beyond the immediate task. We investigate privilege escalation targeting this permission-update process. Specifically, adversarial context planted in a repository can reliably steer an agent performing a routine task into requesting a plausibly justified, yet dangerously broad allow-rule. If the user approves the request, the saved rule can effectively blind the harness, enabling adversaries to execute malicious actions that would otherwise require approval, without triggering human oversight. Across production platforms, including Claude Code and Codex CLI, we demonstrate that agents successfully elicit the target allow-rules in 58% of…
PDF arXiv
arXiv cs.CLOct 16
Self-Evolving Agents Can Repair Failures but Struggle to Generalize from Them
Weixiang Zhao, Yichen Zhang, Yanyan Zhao, Jinming Bai, Bin Liang +3
Experience-driven self-evolution offers a promising path for large language model (LLM) agents to continually improve through interaction. Yet existing approaches often rely on successful trajectories or external evaluation signals, leaving unclear whether agents can evolve when such positive anchors are unavailable. We study this question through Cold-Start Failure-Only Evolution (CFOE), where agents start without accumulated experience and learn only from failed interactions without privileged guidance. Across three self-evolving paradigms, multiple LLM backbones, and four interactive environments, we uncover a consistent repair-generalization gap: failure-derived experience can help repair previously failed tasks, but rarely yields sustained improvements on unseen tasks. Further analysis shows that agents often misinterpret failures, while even verified-correct experience remains difficult to transfer. Relaxing CFOE reveals that verifier feedback and additional interaction provide limited gains, whereas successful trajectories substantially improve transfer. These findings suggest that current self-evolving agents are better at repairing observed failures than building…
PDF arXiv
arXiv cs.CROct 16
SAFE-Mem: Secure Agentic Memory with Fine-Grained Authority and Governance-Aware Retrieval
Haoming Li, Dingyi Kang, Xu Hu, Jinxin Yang, Zhu Xu +6
Large language model (LLM) agents increasingly rely on persistent memory to support long-term reasoning and personalized interactions. However, information collected from external sources can introduce security risks when untrusted claims (i.e., atomic statements) are later treated as authorized preferences, policies, or permissions. Existing defenses typically rely on content-based filtering or coarse-grained provenance tracking, which either fail to prevent unauthorized memory use or block legitimate information, sacrificing memory utility. To address these limitations, we propose SAFE-Mem, a fine-grained memory governance framework that improves security while preserving legitimate memory use. First, SAFE-Mem annotates each claim with three governance labels (i.e., kind, authority, and scope) to capture its provenance and permitted use. Second, governance-aware retrieval matches query requirements with claim permissions to block unauthorized influence while retaining legitimate information. Third, a governance-enabled memory graph connects source sentences, claims, and entities to support provenance tracking and efficient retrieval. Across four LLM backbones on MPBench and…
PDF arXiv
arXiv cs.CLOct 16
Recognizing Atari and Assessing Sente: Controlled Evaluation Tasks for Language-Based Go Agents
Taiyi Chen, Iskar Deng, Amandalynne Paullada
Domain terminology provides concise descriptions of game states and strategic relationships, but whether game-playing language models can accurately recognize these concepts and relate them to move evaluation remains unclear. We construct a controlled evaluation set centered on atari and its relationship to sente in Go, examining terminology recognition under local board changes and move evaluation under different strategic conditions. We conduct our tests on LoGos, a Go language model, after fine-tuning it with terminology and commentary supervision. The model struggles to recognize changes in the atari relationship and assess its strategic implications. Its terminology predictions are associated with move preferences even when those preferences contradict the reference evaluation. Our controlled tests provide a framework for examining whether language-based Go agents connect domain terminology with precise board relationships and strategic move values.
PDF arXiv

Papers

Top tools here

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 papers74 this week
skills · tasks · ai308 papers57 this week
ai agents · indirect prompt injection · adversarial314 papers42 this week
tool using agents · agent systems · multi agent209 papers42 this week
recursive self improvement · harness evolution · adaptive260 papers41 this week
vision language · safety evaluations · safety alignment115 papers33 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 papers in Research

arXiv cs.AIOct 16
Adaptation of Computer Use Agents to New Software with User Manuals
Chulhoon Yoon, Kyungri Park, Jinyeong Yun, Daeyun Jeong, Sukmin Yun +1
Recent advances in multimodal foundation models have significantly improved the ability to operate real-world software through graphical user interfaces (GUIs) using visual observations and natural language instructions. However, maintaining strong performance on unseen applications remains challenging, as practical tasks often require long-horizon procedural knowledge and application-specific operation patterns. In addition, constructing executable tasks and collecting corresponding trajectories for training still heavily rely on human-annotated data, incurring substantial cost. In this paper, we propose MAGE (Manual-augmented computer use AGent Execution and learning), a framework that leverages user manuals as a scalable source of procedural knowledge to improve both inference and training. During execution, MAGE retrieves task-relevant manual content and uses a planner to generate explicit procedural guidance for CUAs. To further account for the discrepancies between manuals and actual GUI environments, such as version differences or user settings, MAGE also adapts the agent policy through environment interaction. During training, MAGE converts manuals into executable…
PDF arXiv
arXiv cs.HCOct 16
PersonaCUABench: Benchmarking Computer-Use Agents Serving Diverse Personas
Haoming Li, Defu Cao, Xu Hu, Yingtao Luo, Yiyang Wang +21
Computer-use agents (CUAs) are commonly evaluated against fixed task specifications, but users may omit requirements, add constraints, or require approval before consequential actions. Completing the benchmark instruction does not guarantee serving the person who asked. We introduce PersonaCUABench, which holds 74 tasks across 11 domains fixed and varies only who asks. A persona agent, playing one of 44 personas in four groups, rewrites each instruction in that user's own words and answers the CUA's questions, while the CUA sees only the dialogue, never the profile or the original instruction. Across nine backbones (3,330 runs), mean task completion falls by 36% relative to the original requests. The losses have two sources: in 59.9% of cells that lose points, the agent engages with what the user asked for and still gets it wrong, and 11.2% are stops the user requested that the checker scores as failures. Even when a persona request is fully completed, the user gives the top satisfaction rating in only 62.7% of cells, against 84.1% for the original requests. CUAs should therefore be evaluated not only on task completion but also on whether they meet the requirements and…
PDF arXiv
arXiv cs.AIOct 16
From Approval to Abuse: Privilege Escalation Attacks on Harness-Protected Coding Agents
Xianglin Yang, Yechao Zhang, Jiahao Liu, Yulin Chen, Yuxin Cao +2
Coding agents use execution harnesses to constrain the consequences of prompt injection by enforcing permission boundaries. These systems deterministically intercept any out-of-sandbox action and prompt the user for approval. To reduce approval fatigue, users can approve allow-rules proposed by the agent, after which any future action matching the rule is allowed without a prompt. In this work, we show that this mechanism introduces a new risk: untrusted context can influence the permissions presented for approval, with consequences extending beyond the immediate task. We investigate privilege escalation targeting this permission-update process. Specifically, adversarial context planted in a repository can reliably steer an agent performing a routine task into requesting a plausibly justified, yet dangerously broad allow-rule. If the user approves the request, the saved rule can effectively blind the harness, enabling adversaries to execute malicious actions that would otherwise require approval, without triggering human oversight. Across production platforms, including Claude Code and Codex CLI, we demonstrate that agents successfully elicit the target allow-rules in 58% of…
PDF arXiv
arXiv cs.CLOct 16
Self-Evolving Agents Can Repair Failures but Struggle to Generalize from Them
Weixiang Zhao, Yichen Zhang, Yanyan Zhao, Jinming Bai, Bin Liang +3
Experience-driven self-evolution offers a promising path for large language model (LLM) agents to continually improve through interaction. Yet existing approaches often rely on successful trajectories or external evaluation signals, leaving unclear whether agents can evolve when such positive anchors are unavailable. We study this question through Cold-Start Failure-Only Evolution (CFOE), where agents start without accumulated experience and learn only from failed interactions without privileged guidance. Across three self-evolving paradigms, multiple LLM backbones, and four interactive environments, we uncover a consistent repair-generalization gap: failure-derived experience can help repair previously failed tasks, but rarely yields sustained improvements on unseen tasks. Further analysis shows that agents often misinterpret failures, while even verified-correct experience remains difficult to transfer. Relaxing CFOE reveals that verifier feedback and additional interaction provide limited gains, whereas successful trajectories substantially improve transfer. These findings suggest that current self-evolving agents are better at repairing observed failures than building…
PDF arXiv
arXiv cs.CROct 16
SAFE-Mem: Secure Agentic Memory with Fine-Grained Authority and Governance-Aware Retrieval
Haoming Li, Dingyi Kang, Xu Hu, Jinxin Yang, Zhu Xu +6
Large language model (LLM) agents increasingly rely on persistent memory to support long-term reasoning and personalized interactions. However, information collected from external sources can introduce security risks when untrusted claims (i.e., atomic statements) are later treated as authorized preferences, policies, or permissions. Existing defenses typically rely on content-based filtering or coarse-grained provenance tracking, which either fail to prevent unauthorized memory use or block legitimate information, sacrificing memory utility. To address these limitations, we propose SAFE-Mem, a fine-grained memory governance framework that improves security while preserving legitimate memory use. First, SAFE-Mem annotates each claim with three governance labels (i.e., kind, authority, and scope) to capture its provenance and permitted use. Second, governance-aware retrieval matches query requirements with claim permissions to block unauthorized influence while retaining legitimate information. Third, a governance-enabled memory graph connects source sentences, claims, and entities to support provenance tracking and efficient retrieval. Across four LLM backbones on MPBench and…
PDF arXiv
arXiv cs.CLOct 16
Recognizing Atari and Assessing Sente: Controlled Evaluation Tasks for Language-Based Go Agents
Taiyi Chen, Iskar Deng, Amandalynne Paullada
Domain terminology provides concise descriptions of game states and strategic relationships, but whether game-playing language models can accurately recognize these concepts and relate them to move evaluation remains unclear. We construct a controlled evaluation set centered on atari and its relationship to sente in Go, examining terminology recognition under local board changes and move evaluation under different strategic conditions. We conduct our tests on LoGos, a Go language model, after fine-tuning it with terminology and commentary supervision. The model struggles to recognize changes in the atari relationship and assess its strategic implications. Its terminology predictions are associated with move preferences even when those preferences contradict the reference evaluation. Our controlled tests provide a framework for examining whether language-based Go agents connect domain terminology with precise board relationships and strategic move values.
PDF arXiv

Activity · 12 weeks

Fields · AI · updated 25m ago

Agents

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

484papers this week−9.4% vs last week

All papers · 2.5k With code · 116

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.