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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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Recently funded

Topics in this field

coding agents · code generation · coding agent318 papers72 this week
recursive self improvement · harness evolution · adaptive278 papers41 this week
skills · tasks · ai321 papers39 this week
agent · agentic · llm agents179 papers39 this week
ai agents · indirect prompt injection · adversarial328 papers35 this week
tool using agents · agent systems · multi agent223 papers35 this week

Topics in this field

Active contributors · 90 days

Active contributors

Activity · 12 weeks

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All papers in Research

arXiv cs.SEOct 10
Specification-Based Testing of Large Language Models
Kausar Moshood, Jace Bolante, Roshan Aditya Murali, Manish Motwani
Model cards, system cards, and safety reports increasingly document LLM safety behaviors, yet safety evaluations typically start from predefined benchmarks rather than asking which documented claims those benchmarks actually test. We introduce LEST (LLM Evaluation through Specification-driven Testing), a framework that treats documented safety claims as specifications for black-box testing. In LEST, each claim determines which benchmark prompt tests it and whether a Refuse-to-Comply transition, where a model refuses a prompt but complies with its mutated version, counts as evidence against it. LEST mutates these prompts to search for counterexamples, manually validates each candidate, and traces validated counterexamples back to the claims they contradict. We evaluate LEST using the system cards of Claude Opus 4.8 and GPT-5.5 with 97 seed prompts from SORRY-Bench across Mental Health and Self-Harm, Harmful and Violent Content, and Fairness and Bias. Of 56 identified safety claims, 29 (51.8%) are prompt-testable, but only 17 (58.6%) are exercised by an applicable benchmark prompt. Both models refuse >95% of benchmark prompts. Evaluating 1,836 mutated prompts on both models reveals…
PDF arXiv
arXiv cs.SEOct 9
SPROUT: Scaling Agentic Reinforcement Learning for Long-Horizon Software Engineering
Mengyang Liu, Weiming Wang, Haixin Nan, Yu Wu, Peize Li +4
Reinforcement learning (RL) has driven substantial progress in coding agents by relying on verified successful trajectories as learning signals. However, such trajectories are scarce in long-horizon software engineering tasks, where a minor error can invalidate the entire rollout. Repeatedly restarting from the initial state is commonly used yet inefficient, and resetting only the context with working directory misses state changes such as installed dependencies. Therefore, branching from intermediate steps to preserve earlier progress requires restoring both the agent context and execution environments. In this paper, we present SPROUT (SnaPshot-based RollOUT), an agentic RL rollout framework that scales long-horizon coding tasks by resuming and branching from intermediate states. Specifically, SPROUT was built on top of a lightweight infrastructure that checkpoints both the filesystem and the agent context after each assistant turn. A verifier-guided backtracking policy then selects where to resume a failed attempt and how to proceed. Extensive experiments on long-horizon coding tasks show that SPROUT achieves up to 23.6% improvement over pass@k across various LLMs, while using…
PDF arXiv
arXiv cs.AIOct 9
ANASSA: From High-Level Framework to Low-Level Architecture for Spatial Agentic AI
Constantinos Papantoniou, Brian Hilton
Agentic AI systems operating over geospatial data require more than autonomous reasoning and tool use; they must also support explicit orchestration, spatial validation, governance, provenance, failure handling, and human decision authority. Prior work introduced ANASSA, an agentic AI orchestration framework for spatial intelligence that established high-level components and responsibility boundaries for governed agentic execution (arXiv:2609.14824). However, the framework did not specify how those high-level responsibilities should be systematically decomposed into an implementation-facing low-level architecture. This paper addresses that gap by applying Design Science Research (DSR) to refine the ANASSA framework from high-level architectural (HLA) responsibilities into a low-level architecture (LLA). The resulting design preserves the responsibility boundaries of the foundational framework while translating them into executable architectural structures, including 47 logical roles, versioned task graphs, capability-based agent selection, typed contracts, explicit runtime states, spatial validation mechanisms, governance controls, failure and recovery paths, and provenance…
PDF arXiv
arXiv cs.AIOct 9
ACED-Bench: Evaluating How LLM Agents Acquire and Act on Causal Evidence
Yiwei Chen, Amila Weerasinghe, Siddharth Biswal, Utkrisht Rajkumar, Olcay Boz +2
Large language models are increasingly used as scientific assistants, yet we still lack reliable ways to evaluate whether they can investigate a system rather than merely answer a prompt. A useful evaluation must let agents choose experiments, interpret finite evidence, and make causal decisions, while keeping the environment coherent across repeated observations and interventions and preserving objective ground truth. We introduce ACED-BENCH (Active Causal Evidence and Decision Benchmark), a neuro-symbolic benchmark built around this requirement. The key idea is to separate the readable scientific surface from the formal causal world: LLMs generate natural-language scenarios and task text, while hidden probabilistic graphical models define the causal graph, data-generating process, intervention semantics, validation checks, and oracle answers. This yields scientific worlds that are natural for agents to investigate but exact enough for scalable, programmatic evaluation. The main leaderboard ACED-CORE contains 180 tasks: 60 set-valued causal ancestry/descendancy questions and 120 constrained causal-decision tasks. We also report ACED-QUAL, a 150-question yes/no qualification…
PDF arXiv
arXiv cs.LGOct 9
ConformalGuard: False-Positive-Controlled Action Gating for LM Classifiers
Bishnu Bista, Dhanraj Haibatrao Jadhav, Vibha Choudhary, Gaurav Saxena, Arun Pandiyan Perumal +2
Language model classifiers are increasingly deployed for high-stakes decisions such as phishing screening, content moderation, and fraud triage, yet validation-set thresholds provide no finite-sample control of deployment-time false positives. We present ConformalGuard, a model-agnostic calibration layer that wraps a fixed scoring function with finite-sample control over the rate at which legitimate inputs trigger an intervention tier. Our contribution is empirical and operational, not a new conformal theorem: zero-shot LM API scores can be coarse enough (10--11 unique values here) to make temperature scaling and isotonic regression operationally identical to raw thresholding for low-FPR action gating. We compare these methods with conformal calibration for fine-tuned BERT, Claude Sonnet 4, and GPT-4o on public email corpora. For Claude, conformal calibration reduces FPR from 0.040 to 0.008 while recall falls from 0.938 to 0.875. For deterministic GPT-4o scores, the 1% operating point is too conservative to deploy (recall 0.564); three-run stochastic averaging raises recall to 0.732 at FPR 0.005. At 2% prevalence with BERT, conformal calibration removes 25 false-positive reviews…
PDF arXiv
arXiv cs.AIOct 9
Actionable Meta-Requirements for AI Robustness Auditing
Joshua Owotogbe, Roya Nasiri, Indika Kumara, Damian Andrew Tamburri, Willem-Jan van den Heuvel
AI-enabled systems must remain dependable when the conditions under which they operate change (e.g., due to input perturbations, distribution shifts, or changes in external dependencies). This makes robustness auditing of such systems important for judging whether the available evidence is sufficient to support a defined robustness conclusion. However, designing an AI robustness auditing framework is challenging. A key unresolved issue is determining the requirements such a framework should support; these requirements have not yet been consolidated. We address this gap through meta-requirements: higher-order requirements specifying what a class of auditing frameworks should support. In design science research, literature and domain experts are common sources of meta-requirements; accordingly, we derive the initial set from literature and validate and refine it with practitioners. Our systematic literature review retained 74 of 2,890 records and derived 43 meta-requirements across 16 categories. We evaluated them with 128 practitioners, obtaining 5,504 ratings and 586 substantive comments. Overall, 81.20% of ratings indicated agreement or strong agreement. In particular, 41 of the…
PDF arXiv

Papers

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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%

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

Topics in this field

coding agents · code generation · coding agent318 papers72 this week
recursive self improvement · harness evolution · adaptive278 papers41 this week
skills · tasks · ai321 papers39 this week
agent · agentic · llm agents179 papers39 this week
ai agents · indirect prompt injection · adversarial328 papers35 this week
tool using agents · agent systems · multi agent223 papers35 this week

Active contributors · 90 days

Activity · 12 weeks

Fields · AI · updated 2h ago

Agents

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

449papers this week−24.4% vs last week

All papers · 2.7k With code · 143

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2.4k papers this month

Recent papers

All papers in Research

arXiv cs.SEOct 10
Specification-Based Testing of Large Language Models
Kausar Moshood, Jace Bolante, Roshan Aditya Murali, Manish Motwani
Model cards, system cards, and safety reports increasingly document LLM safety behaviors, yet safety evaluations typically start from predefined benchmarks rather than asking which documented claims those benchmarks actually test. We introduce LEST (LLM Evaluation through Specification-driven Testing), a framework that treats documented safety claims as specifications for black-box testing. In LEST, each claim determines which benchmark prompt tests it and whether a Refuse-to-Comply transition, where a model refuses a prompt but complies with its mutated version, counts as evidence against it. LEST mutates these prompts to search for counterexamples, manually validates each candidate, and traces validated counterexamples back to the claims they contradict. We evaluate LEST using the system cards of Claude Opus 4.8 and GPT-5.5 with 97 seed prompts from SORRY-Bench across Mental Health and Self-Harm, Harmful and Violent Content, and Fairness and Bias. Of 56 identified safety claims, 29 (51.8%) are prompt-testable, but only 17 (58.6%) are exercised by an applicable benchmark prompt. Both models refuse >95% of benchmark prompts. Evaluating 1,836 mutated prompts on both models reveals…
PDF arXiv
arXiv cs.SEOct 9
SPROUT: Scaling Agentic Reinforcement Learning for Long-Horizon Software Engineering
Mengyang Liu, Weiming Wang, Haixin Nan, Yu Wu, Peize Li +4
Reinforcement learning (RL) has driven substantial progress in coding agents by relying on verified successful trajectories as learning signals. However, such trajectories are scarce in long-horizon software engineering tasks, where a minor error can invalidate the entire rollout. Repeatedly restarting from the initial state is commonly used yet inefficient, and resetting only the context with working directory misses state changes such as installed dependencies. Therefore, branching from intermediate steps to preserve earlier progress requires restoring both the agent context and execution environments. In this paper, we present SPROUT (SnaPshot-based RollOUT), an agentic RL rollout framework that scales long-horizon coding tasks by resuming and branching from intermediate states. Specifically, SPROUT was built on top of a lightweight infrastructure that checkpoints both the filesystem and the agent context after each assistant turn. A verifier-guided backtracking policy then selects where to resume a failed attempt and how to proceed. Extensive experiments on long-horizon coding tasks show that SPROUT achieves up to 23.6% improvement over pass@k across various LLMs, while using…
PDF arXiv
arXiv cs.AIOct 9
ANASSA: From High-Level Framework to Low-Level Architecture for Spatial Agentic AI
Constantinos Papantoniou, Brian Hilton
Agentic AI systems operating over geospatial data require more than autonomous reasoning and tool use; they must also support explicit orchestration, spatial validation, governance, provenance, failure handling, and human decision authority. Prior work introduced ANASSA, an agentic AI orchestration framework for spatial intelligence that established high-level components and responsibility boundaries for governed agentic execution (arXiv:2609.14824). However, the framework did not specify how those high-level responsibilities should be systematically decomposed into an implementation-facing low-level architecture. This paper addresses that gap by applying Design Science Research (DSR) to refine the ANASSA framework from high-level architectural (HLA) responsibilities into a low-level architecture (LLA). The resulting design preserves the responsibility boundaries of the foundational framework while translating them into executable architectural structures, including 47 logical roles, versioned task graphs, capability-based agent selection, typed contracts, explicit runtime states, spatial validation mechanisms, governance controls, failure and recovery paths, and provenance…
PDF arXiv
arXiv cs.AIOct 9
ACED-Bench: Evaluating How LLM Agents Acquire and Act on Causal Evidence
Yiwei Chen, Amila Weerasinghe, Siddharth Biswal, Utkrisht Rajkumar, Olcay Boz +2
Large language models are increasingly used as scientific assistants, yet we still lack reliable ways to evaluate whether they can investigate a system rather than merely answer a prompt. A useful evaluation must let agents choose experiments, interpret finite evidence, and make causal decisions, while keeping the environment coherent across repeated observations and interventions and preserving objective ground truth. We introduce ACED-BENCH (Active Causal Evidence and Decision Benchmark), a neuro-symbolic benchmark built around this requirement. The key idea is to separate the readable scientific surface from the formal causal world: LLMs generate natural-language scenarios and task text, while hidden probabilistic graphical models define the causal graph, data-generating process, intervention semantics, validation checks, and oracle answers. This yields scientific worlds that are natural for agents to investigate but exact enough for scalable, programmatic evaluation. The main leaderboard ACED-CORE contains 180 tasks: 60 set-valued causal ancestry/descendancy questions and 120 constrained causal-decision tasks. We also report ACED-QUAL, a 150-question yes/no qualification…
PDF arXiv
arXiv cs.LGOct 9
ConformalGuard: False-Positive-Controlled Action Gating for LM Classifiers
Bishnu Bista, Dhanraj Haibatrao Jadhav, Vibha Choudhary, Gaurav Saxena, Arun Pandiyan Perumal +2
Language model classifiers are increasingly deployed for high-stakes decisions such as phishing screening, content moderation, and fraud triage, yet validation-set thresholds provide no finite-sample control of deployment-time false positives. We present ConformalGuard, a model-agnostic calibration layer that wraps a fixed scoring function with finite-sample control over the rate at which legitimate inputs trigger an intervention tier. Our contribution is empirical and operational, not a new conformal theorem: zero-shot LM API scores can be coarse enough (10--11 unique values here) to make temperature scaling and isotonic regression operationally identical to raw thresholding for low-FPR action gating. We compare these methods with conformal calibration for fine-tuned BERT, Claude Sonnet 4, and GPT-4o on public email corpora. For Claude, conformal calibration reduces FPR from 0.040 to 0.008 while recall falls from 0.938 to 0.875. For deterministic GPT-4o scores, the 1% operating point is too conservative to deploy (recall 0.564); three-run stochastic averaging raises recall to 0.732 at FPR 0.005. At 2% prevalence with BERT, conformal calibration removes 25 false-positive reviews…
PDF arXiv
arXiv cs.AIOct 9
Actionable Meta-Requirements for AI Robustness Auditing
Joshua Owotogbe, Roya Nasiri, Indika Kumara, Damian Andrew Tamburri, Willem-Jan van den Heuvel
AI-enabled systems must remain dependable when the conditions under which they operate change (e.g., due to input perturbations, distribution shifts, or changes in external dependencies). This makes robustness auditing of such systems important for judging whether the available evidence is sufficient to support a defined robustness conclusion. However, designing an AI robustness auditing framework is challenging. A key unresolved issue is determining the requirements such a framework should support; these requirements have not yet been consolidated. We address this gap through meta-requirements: higher-order requirements specifying what a class of auditing frameworks should support. In design science research, literature and domain experts are common sources of meta-requirements; accordingly, we derive the initial set from literature and validate and refine it with practitioners. Our systematic literature review retained 74 of 2,890 records and derived 43 meta-requirements across 16 categories. We evaluated them with 128 practitioners, obtaining 5,504 ratings and 586 substantive comments. Overall, 81.20% of ratings indicated agreement or strong agreement. In particular, 41 of the…
PDF arXiv

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.