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Open problems in Agents

Evaluating long-horizon agentic behavior in real-world tasks4 papers raise it · 3 address it
Current benchmarks often simplify or omit the complex, multi-stage reasoning and exploration required for real-world professional tasks, limiting the assessment of true agentic capabilities.
- 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 · addresses
- Into the Unknown: Benchmarking AI agents on open scientific research problemsraises
Faithful agent development from natural-language requirements1 paper raises it · 1 addresses it
Agents often fail to deliver systems that meet the actual requirements, instead producing outputs that only appear correct.
- AgentSWE: Can Coding Agents Build the Agent You Actually Want?raises · addresses
Learning from experience in unfamiliar environments1 paper raises it · 1 addresses it
Agents struggle to acquire and retain new knowledge through interaction in novel or dynamic settings, rather than relying on pretrained knowledge.
- Learn2Play Bench: How Well Do LLM Agents Learn from Experience in Unfamiliar Environments?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, leading to missed or misapplied information in long-term interactions.
- RealCompanion: Benchmarking Human Understanding from Reasoning over Longitudinal Real-World Conversationsraises
Open-source, reproducible stacks for generalist computer-use agents1 paper raises it · 2 address it
Frontier implementations of generalist computer-use agents are often closed, limiting systematic study and community collaboration.
- OpenGCUA: Toward a Fully Open Stack for Generalist Computer Useraises · addresses
- MAI-Gym: An Open All-in-One Platform for GUI Agent Evaluation and RL Training across Mobile and Computer Environmentsaddresses

Named by an AI from these papers' abstracts.

Open problems

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Topics in this field

coding agents · code generation · coding agent318 papers67 this week
recursive self improvement · harness evolution · adaptive278 papers40 this week
agent · agentic · llm agents179 papers37 this week
ai agents · indirect prompt injection · adversarial328 papers35 this week
skills · tasks · ai321 papers35 this week
tool using agents · agent systems · multi agent223 papers35 this week

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Active contributors · 90 days

Active contributors

Activity · 12 weeks

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All papers in Research

arXiv cs.AIOct 8
InterviewPlayground: A Validated Simulation Environment for Evaluating AI Interviewers
Jonathan Ivey, Aimee Liang, Arthur Y. S. Wang, Madeline Mandell, Ziang Xiao +1
Increasingly, AI interviewers are being developed to elicit open-ended responses in applications like market research, public polling, preference elicitation, and social science research. However, evaluating AI interviewers is challenging because they function in extended, multi-turn interactions where they must adapt to participant behaviors. To address this need, we develop InterviewPlayground, a simulation environment for evaluating AI interviewers using simulated study participants whose behaviors are grounded in social theory. Simulated studies in InterviewPlayground produce an InterviewReportCard, which assesses the performance of AI interviewers using a suite of validated measures. To test whether our simulation-based evaluations predict performance with human participants, we conduct 15 real qualitative studies with five AI interviewers, three interview topics, and 450 human participants and compare them to simulated studies in InterviewPlayground. We find that AI interviewer performance in InterviewPlayground predicts performance in human studies with an average Pearson correlation of 0.86 across 12 measures, and the simulated interactions from InterviewPlayground…
PDF arXiv
arXiv cs.AIOct 8
Complexity of Grounded Semantics and Preferred Semantics in Finitary Argumentation Frameworks
Jinfan Xu, Jieting Luo
Abstract argumentation frameworks (AFs) introduced by Dung provide a formal foundation for non-monotonic reasoning in artificial intelligence. While decision problems for general infinite AFs typically reside at high levels of the analytical hierarchy ($Σ_1^1$ or $Π_1^1$), restricting the framework to be computably finitary reduces some of the complexity to the arithmetical hierarchy. In this paper, we present a complexity mapping of grounded and preferred semantics in computably finitary AFs across standard decision problems: credulous acceptance ($\Cred$), skeptical acceptance ($\Skep$), extension existence ($\Ex$), uniqueness ($\Uni$), and non-empty existence ($\NE$). For grounded semantics, credulous and skeptical acceptance are already known to be $Σ_1^0$-complete. We show that non-empty existence is also $Σ_1^0$-complete, whereas existence and uniqueness are trivial. These classifications are understood within the domain of valid computably finitary representations. For preferred semantics, using a computably finitely branching computation tree, $\Cred_{\pref}$ is shown to be in $Π_1^0$-c and $\NE_{\pref}$ is $Σ_2^0$-c. However, it is insufficient to reduce universal…
PDF arXiv
arXiv cs.LGOct 8
Agentic-TTT: Training test-time policy for test-time training
Jiahao Lu, Mohan Kankanhalli
Test-time training (TTT) adapts an LLM's parameters using signals derived from test inputs, and can make striking improvements in pre-specified settings such as IMO competitions or designated open problems. By turning deployment experience into parameter updates, TTT provides a direct mechanism for model-level self-improvement. Yet TTT is not universally beneficial: each TTT algorithm works in different settings, and applying an ill-suited method could waste test-time compute or even damage model performance. Therefore, such parameter-level self-improvement requires agency: the model must decide when TTT is warranted, which algorithm to invoke, and whether an existing skill can be reused. To fill this gap, we introduce Agentic-TTT, which learns a test-time policy to govern those decisions. Agentic-TTT turns TTT procedures into callable tools, treats accumulated skills as an evolving deployment environment, and trains its policy using the observed utility gains from its decisions. On our benchmark, Agentic-TTT nearly doubles the utility over the backbone model, learns to trade off utility against compute, and generalizes to domains unseen during training. Together, these results…
PDF arXiv
arXiv cs.CVOct 8
FearCaut-Qwen: Affective Steering in a Vision-Language Model Shifts the Decision Criterion for Hazard Assessment
Xiaoshan Zhou
Vision-language models (VLMs) show great potential for damage assessment after a disaster, but a recurring deficiency is that they are reluctant to declare a hazard; that is, recall is low even when overall accuracy appears adequate. This study examines that deficiency by using signal detection theory to decompose the decision behavior into perceptual capability and decision-criterion placement. We then propose a novel method for correcting the over-conservative decision policy, inspired by the finding that fear makes humans risk-averse, and ask whether an affective representation associated with fear can be causally manipulated to similarly alter a VLM's decision tendency. Using mechanistic interpretability, we localize a causally implicated affective circuit in the model and use activation steering to manipulate it while observing the effect on downstream prediction. The method is tested on a two-stage SeisMLLM pipeline built on Qwen2.5-VL-7B-Instruct, which flags only 27.0% of genuinely unsafe buildings on the SeisMLLM-1K test split and never issues a false Red, an SDT criterion of c = +1.354, despite adequate evidence quality (d' = 1.521). An affective direction is localized…
PDF arXiv
arXiv cs.AIOct 8
MindFlow: Mind Supernet Powered Thinking Flows for Research Idea Innovation
Mengdi Liu, Wenjue Chen, Wenyue Chen, Cheng Yang, Fanqi Kong +8
Research idea innovation is a fundamental engine of scientific progress, yet it remains difficult to generate and evaluate in a scalable and controllable way. This challenge lies in its inherently open-ended and multi-objective nature, where ideas should balance novelty, plausibility and feasibility. While recent LLM-based approaches have made progress through carefully designed prompts or agent pipelines, they are constrained by predefined, static ideation workflows. To address this limitation, we propose MindFlow, a framework that explicitly formulates ideation as a graph-structured Flow in Mind, which is composed of modular thinking operators and modeled by a probabilistic mind supernet. Given a research topic, a controller dynamically samples thinking flows to generate candidate ideas. This open-ended problem is optimized using a tournament-based relative ranking, enabling the controller to progressively favor higher-quality thinking flows. We further introduce an evaluation protocol that jointly assesses problem finding and problem solving, going beyond title- or abstractonly judgments. Across diverse topics, MindFlow shows its superiority as an explicit, controllable and…
PDF arXiv
arXiv cs.SEOct 8
Can LLMs Fix It Without Code? Toward Automated Verification of No-Code Bug Fixes
Utku Boran Torun, Veli Karakaya, Eray Tüzün
A no-code fix resolves an invalid bug report by directing the user to change a setting, update to a version where the problem is already fixed, or adjust their workflow. Manually verifying whether a proposed no-code fix resolves the reported bug takes considerable developer time. This study proposes an automated, execution-based pipeline for evaluating the capability of large language models (LLMs) to generate no-code fixes in a real browser environment. We evaluate 322 no-code fixes generated by the 12 configurations released with the benchmark of a previous study, covering bug reports categorized as Faulty Configuration, Wrong Version, or External System & Dependency. An executor agent applies each fix by following its natural-language instructions, and an issue-specific checker determines whether the reported bug persists. We repeat the pipeline with three executors: two Computer-Use Agents, OpenCUA-72B and Claude Sonnet 5, and one multimodal agentic LLM, Meta's Muse Glimmer. Only 17.6% of the candidate issues could be set up and passed both sanity gates. Across the 322 fixes, 14.6% to 49.7% resolved the bug depending on the executor, and the strongest configuration, Claude…
PDF arXiv

Papers

Top tools here

All tools

QwenPaw+467% downloads · 29.9k/wk+466.8%
jcode+233% downloads · 25.5k/wk+233.1%
billion-context+259% downloads · 1.1M/wk+258.8%
oh-my-claudecode+47% downloads · 7.7k/wk+47.4%
TanStack AI+68% downloads · 1M/wk+67.7%

Topics in this field

coding agents · code generation · coding agent318 papers67 this week
recursive self improvement · harness evolution · adaptive278 papers40 this week
agent · agentic · llm agents179 papers37 this week
ai agents · indirect prompt injection · adversarial328 papers35 this week
skills · tasks · ai321 papers35 this week
tool using agents · agent systems · multi agent223 papers35 this week

Recently funded

Market

Pathos AISeries B+ · Oct 23$237M
Tolan AI Companion AppStage not given · Oct 23$30M
hexafarmsStage not given · Oct 23$5.4M
CygridPre-seed · Oct 23$3M
SunniesSeed · Oct 23$318.9k

Active contributors · 90 days

Recent papers

All papers in Research

arXiv cs.AIOct 8
InterviewPlayground: A Validated Simulation Environment for Evaluating AI Interviewers
Jonathan Ivey, Aimee Liang, Arthur Y. S. Wang, Madeline Mandell, Ziang Xiao +1
Increasingly, AI interviewers are being developed to elicit open-ended responses in applications like market research, public polling, preference elicitation, and social science research. However, evaluating AI interviewers is challenging because they function in extended, multi-turn interactions where they must adapt to participant behaviors. To address this need, we develop InterviewPlayground, a simulation environment for evaluating AI interviewers using simulated study participants whose behaviors are grounded in social theory. Simulated studies in InterviewPlayground produce an InterviewReportCard, which assesses the performance of AI interviewers using a suite of validated measures. To test whether our simulation-based evaluations predict performance with human participants, we conduct 15 real qualitative studies with five AI interviewers, three interview topics, and 450 human participants and compare them to simulated studies in InterviewPlayground. We find that AI interviewer performance in InterviewPlayground predicts performance in human studies with an average Pearson correlation of 0.86 across 12 measures, and the simulated interactions from InterviewPlayground…
PDF arXiv
arXiv cs.AIOct 8
Complexity of Grounded Semantics and Preferred Semantics in Finitary Argumentation Frameworks
Jinfan Xu, Jieting Luo
Abstract argumentation frameworks (AFs) introduced by Dung provide a formal foundation for non-monotonic reasoning in artificial intelligence. While decision problems for general infinite AFs typically reside at high levels of the analytical hierarchy ($Σ_1^1$ or $Π_1^1$), restricting the framework to be computably finitary reduces some of the complexity to the arithmetical hierarchy. In this paper, we present a complexity mapping of grounded and preferred semantics in computably finitary AFs across standard decision problems: credulous acceptance ($\Cred$), skeptical acceptance ($\Skep$), extension existence ($\Ex$), uniqueness ($\Uni$), and non-empty existence ($\NE$). For grounded semantics, credulous and skeptical acceptance are already known to be $Σ_1^0$-complete. We show that non-empty existence is also $Σ_1^0$-complete, whereas existence and uniqueness are trivial. These classifications are understood within the domain of valid computably finitary representations. For preferred semantics, using a computably finitely branching computation tree, $\Cred_{\pref}$ is shown to be in $Π_1^0$-c and $\NE_{\pref}$ is $Σ_2^0$-c. However, it is insufficient to reduce universal…
PDF arXiv
arXiv cs.LGOct 8
Agentic-TTT: Training test-time policy for test-time training
Jiahao Lu, Mohan Kankanhalli
Test-time training (TTT) adapts an LLM's parameters using signals derived from test inputs, and can make striking improvements in pre-specified settings such as IMO competitions or designated open problems. By turning deployment experience into parameter updates, TTT provides a direct mechanism for model-level self-improvement. Yet TTT is not universally beneficial: each TTT algorithm works in different settings, and applying an ill-suited method could waste test-time compute or even damage model performance. Therefore, such parameter-level self-improvement requires agency: the model must decide when TTT is warranted, which algorithm to invoke, and whether an existing skill can be reused. To fill this gap, we introduce Agentic-TTT, which learns a test-time policy to govern those decisions. Agentic-TTT turns TTT procedures into callable tools, treats accumulated skills as an evolving deployment environment, and trains its policy using the observed utility gains from its decisions. On our benchmark, Agentic-TTT nearly doubles the utility over the backbone model, learns to trade off utility against compute, and generalizes to domains unseen during training. Together, these results…
PDF arXiv
arXiv cs.CVOct 8
FearCaut-Qwen: Affective Steering in a Vision-Language Model Shifts the Decision Criterion for Hazard Assessment
Xiaoshan Zhou
Vision-language models (VLMs) show great potential for damage assessment after a disaster, but a recurring deficiency is that they are reluctant to declare a hazard; that is, recall is low even when overall accuracy appears adequate. This study examines that deficiency by using signal detection theory to decompose the decision behavior into perceptual capability and decision-criterion placement. We then propose a novel method for correcting the over-conservative decision policy, inspired by the finding that fear makes humans risk-averse, and ask whether an affective representation associated with fear can be causally manipulated to similarly alter a VLM's decision tendency. Using mechanistic interpretability, we localize a causally implicated affective circuit in the model and use activation steering to manipulate it while observing the effect on downstream prediction. The method is tested on a two-stage SeisMLLM pipeline built on Qwen2.5-VL-7B-Instruct, which flags only 27.0% of genuinely unsafe buildings on the SeisMLLM-1K test split and never issues a false Red, an SDT criterion of c = +1.354, despite adequate evidence quality (d' = 1.521). An affective direction is localized…
PDF arXiv
arXiv cs.AIOct 8
MindFlow: Mind Supernet Powered Thinking Flows for Research Idea Innovation
Mengdi Liu, Wenjue Chen, Wenyue Chen, Cheng Yang, Fanqi Kong +8
Research idea innovation is a fundamental engine of scientific progress, yet it remains difficult to generate and evaluate in a scalable and controllable way. This challenge lies in its inherently open-ended and multi-objective nature, where ideas should balance novelty, plausibility and feasibility. While recent LLM-based approaches have made progress through carefully designed prompts or agent pipelines, they are constrained by predefined, static ideation workflows. To address this limitation, we propose MindFlow, a framework that explicitly formulates ideation as a graph-structured Flow in Mind, which is composed of modular thinking operators and modeled by a probabilistic mind supernet. Given a research topic, a controller dynamically samples thinking flows to generate candidate ideas. This open-ended problem is optimized using a tournament-based relative ranking, enabling the controller to progressively favor higher-quality thinking flows. We further introduce an evaluation protocol that jointly assesses problem finding and problem solving, going beyond title- or abstractonly judgments. Across diverse topics, MindFlow shows its superiority as an explicit, controllable and…
PDF arXiv
arXiv cs.SEOct 8
Can LLMs Fix It Without Code? Toward Automated Verification of No-Code Bug Fixes
Utku Boran Torun, Veli Karakaya, Eray Tüzün
A no-code fix resolves an invalid bug report by directing the user to change a setting, update to a version where the problem is already fixed, or adjust their workflow. Manually verifying whether a proposed no-code fix resolves the reported bug takes considerable developer time. This study proposes an automated, execution-based pipeline for evaluating the capability of large language models (LLMs) to generate no-code fixes in a real browser environment. We evaluate 322 no-code fixes generated by the 12 configurations released with the benchmark of a previous study, covering bug reports categorized as Faulty Configuration, Wrong Version, or External System & Dependency. An executor agent applies each fix by following its natural-language instructions, and an issue-specific checker determines whether the reported bug persists. We repeat the pipeline with three executors: two Computer-Use Agents, OpenCUA-72B and Claude Sonnet 5, and one multimodal agentic LLM, Meta's Muse Glimmer. Only 17.6% of the candidate issues could be set up and passed both sanity gates. Across the 322 fixes, 14.6% to 49.7% resolved the bug depending on the executor, and the strongest configuration, Claude…
PDF arXiv

Activity · 12 weeks

Fields · AI · updated 1h ago

Agents

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

423papers this week−28.4% vs last week

All papers · 2.7k With code · 143

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2.4k papers this month

Open problems in Agents

Evaluating long-horizon agentic behavior in real-world tasks4 papers raise it · 3 address it
Current benchmarks often simplify or omit the complex, multi-stage reasoning and exploration required for real-world professional tasks, limiting the assessment of true agentic capabilities.
- 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 · addresses
- Into the Unknown: Benchmarking AI agents on open scientific research problemsraises
Faithful agent development from natural-language requirements1 paper raises it · 1 addresses it
Agents often fail to deliver systems that meet the actual requirements, instead producing outputs that only appear correct.
- AgentSWE: Can Coding Agents Build the Agent You Actually Want?raises · addresses
Learning from experience in unfamiliar environments1 paper raises it · 1 addresses it
Agents struggle to acquire and retain new knowledge through interaction in novel or dynamic settings, rather than relying on pretrained knowledge.
- Learn2Play Bench: How Well Do LLM Agents Learn from Experience in Unfamiliar Environments?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, leading to missed or misapplied information in long-term interactions.
- RealCompanion: Benchmarking Human Understanding from Reasoning over Longitudinal Real-World Conversationsraises
Open-source, reproducible stacks for generalist computer-use agents1 paper raises it · 2 address it
Frontier implementations of generalist computer-use agents are often closed, limiting systematic study and community collaboration.
- OpenGCUA: Toward a Fully Open Stack for Generalist Computer Useraises · addresses
- MAI-Gym: An Open All-in-One Platform for GUI Agent Evaluation and RL Training across Mobile and Computer Environmentsaddresses

Named by an AI from these papers' abstracts.