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Open problems in Agents

Long-horizon task completion with exploration and verification3 papers raise it · 2 address it
Agents struggle to accumulate evidence, adjust strategies, and verify results over long, realistic workflows.
- Scaling Agentic Data for Long-Horizon Terminal Intelligenceraises · addresses
- 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 Useraises
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 rather than just appearing to.
- AgentSWE: Can Coding Agents Build the Agent You Actually Want?raises · addresses
Realistic evaluation of agent learning from experience1 paper raises it · 1 addresses it
Existing benchmarks often test reasoning with known rules rather than learning novel, counterintuitive rules through interaction.
- 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
Agents cannot reliably identify when historical context is relevant, leading to missed or incorrect responses.
- RealCompanion: Benchmarking Human Understanding from Reasoning over Longitudinal Real-World Conversationsraises
Open-source, reproducible GUI agent stacks2 papers raise it · 2 address it
Frontier GUI 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 Environmentsraises · addresses

Named by an AI from these papers' abstracts.

Open problems

Recently funded

Market

FirstWorkSeries A · Oct 21$15M
Asymptote LabsSeed · Oct 20$5.5M
LumilabPre-seed · Oct 20undisclosed
GreptileSeries B+ · Oct 19$50M
VesslaPre-seed · Oct 19$3.3M

Recently funded

Topics in this field

coding agents · code generation · coding agent303 papers72 this week
skills · tasks · ai315 papers46 this week
recursive self improvement · harness evolution · adaptive271 papers41 this week
ai agents · indirect prompt injection · adversarial319 papers38 this week
tool using agents · agent systems · multi agent216 papers34 this week
agent · agentic · llm agents172 papers34 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 13
When Should Enterprise Policy Decisions Be Learned, and When Should They Be Reasoned?
Hyojung Han, Ryangkyung Kang, Youngeun Kim, Seung-Hun Jeon
An enterprise agent that answers under a written policy must choose an action before it says anything: answer, ask for a missing fact, ask for confirmation, call one or several tools, escalate, or refuse. Holding one 27B Korean enterprise model fixed, we vary whether the decision is a generated JSON object or a single constrained token, and whether the model may reason first. On 2800 in-distribution items we did not detect an accuracy difference between the two deployed paths (93.46% vs 94.14%), while they differ about tenfold in restart stability (3.00% of actions flip vs 0.29%); they also differ in reasoning, so this is an operational comparison. On a sealed benchmark of 300 items whose labels come from a hidden executable policy, reasoning is worth +17.3 points [+12.3, +23.0] on exact action. With reasoning off, the format difference was 0.0 points [-4.3, +4.0], a failure to detect an advantage for either interface, not equivalence; with reasoning on it is unresolved. Against a token-matched random control, boundary-targeted supervision moves the two covered boundaries by +17.5 and +20.0 points and leaves a held-out boundary unchanged. In a 3-seed training x reasoning…
PDF arXiv
arXiv cs.AIOct 13
Executable Prefix Values for Locating Agent Failures
Bingyu Shen, Jiaqi Tang, Lan Wei, Yi Sheng, J. Jenny Li +1
Work on debugging LLM agents treats the "first mistake" marked by a human or LLM judge as ground truth for where a trajectory failed. We ask whether such labels point at causes or symptoms, and make the distinction executable: we restart the agent from reconstructed intermediate states of a failed trajectory and estimate its prefix value V(h_t), the probability of success from each step, with a certified point of no return (PNR) for a failure. On 150 SWE-bench Verified tasks with two frozen open-weight agents, we build such curves for 47 and 60 failed trajectories and probe every step labeled by four frontier judges under the Traverse rubric. We find that the labels do not mark what a controller needs. Judges of different lineages agree on the exact step in only 21/57 cases, and the newest judges flag mistakes in 8/10 and 10/10 successful runs. For 66% of the labels, the agent's success probability does not drop at the labeled step; only 7/188 answers name the decisive step. Restarting at the measured restart point raises expected success from 0.44 to 0.57 offline, and real restarts confirm the advantage, whereas restarting at a judge's label does not (-0.08 to -0.03). We release…
PDF arXiv
arXiv cs.SEOct 13
StateHarness: Maintaining Evolving Project State for Long-Horizon Coding Agents
Chuan Zou, Yujun Zhou, Xiaonan Luo, Xiangliang Zhang
Long-horizon coding requires agents to maintain an evolving view of the project they are changing: what problem is currently being solved, what has already been learned or modified, which other artifacts may be affected, and what work remains unresolved. When this information is scattered across a growing interaction history, local edits can become disconnected from their later consequences and progress can be difficult to carry forward. We introduce StateHarness, a runtime framework that maintains evolving project state without updating model parameters. The working agent records its current problem and hypothesis, important findings and decisions, discovered repository relationships, and unfinished work. Runtime support prompts record updates, searches for callers, tests, and documentation that may be affected by a change, and maintains a compact working brief for subsequent decisions. When behavioral signals accumulate, a separate diagnostic session revisits the recorded history and surfaces overlooked evidence. Across three repository-level evaluations, StateHarness improves task completion and several measures of change consistency. On RefactorBench, full-chain completion…
PDF arXiv
arXiv cs.AIOct 13
MAS-Gate: Unlocking Multi-Agent System Lifecycle via Node-Centric Quantification
Hehai Lin, Zixuan Wang, Ming Yan, Peidong Wang, Shilei Cao +4
Large language model (LLM)-based multi-agent systems (MAS) can solve complex problems beyond the reach of a single agent by decomposing tasks and coordinating complementary capabilities. Despite extensive research on MAS, existing work largely focuses on specific aspects or individual phases of their development and operation, resulting in fragmented perspectives with objectives and signals that are difficult to reconcile across these different settings. To bridge these disconnected perspectives, we conceptualize MAS development and operation as a three-phase lifecycle spanning design-time orchestration before execution, runtime intervention during execution, and post-hoc attribution after execution, and introduce MAS-Gate, a unified node-centric quantification applicable across these phases. Modeling each execution as a directed acyclic graph, MAS-Gate combines within-node utility, which estimates a node's contribution through changes in reference-answer likelihood, with inter-node strength, which approximates semantic uptake and potential downstream reach. We adapt this shared quantification to three objectives: providing a structure-aware dense reward for Orchestrator…
PDF arXiv
arXiv cs.AIOct 13
Verifier-Harness Co-Evolution for Training-Free Self-Improvement on Open-Ended Tasks
Tianyi Xu, Yaolun Zhang, Yiran Wu, Joe Nguyen, Shengyu Dai +4
Training-free self-improvement lets an agent use feedback to revise the response it is about to return and to update the harness state that shapes how it handles later tasks. On open-ended tasks that feedback rarely comes from reference answers or executable tests, so the agent has to rely on a model-based verifier. When the verifier is wrong, the current response can get worse, and misleading experience can persist across the task stream. A verifier that never changes also grows stale as the agent's responses improve and task demands shift. We introduce Unified Co-Evolution (UCoE), a training-free method that evolves an executable verifier program together with the agent's harness state while all model parameters stay fixed. UCoE synthesizes candidate verifier programs and tests them on response contrasts, pairs of responses that differ in one declared property and have a known preferred side. It selects the candidate whose discrimination on its weakest contrast family is highest, a quantity we call driving capacity. The selected verifier diagnoses one weakness in each draft, guides a single repair, decides which response is served, and determines whether the accepted repair may…
PDF arXiv
arXiv cs.AIOct 12
REA: From Correct LLM Judgments to Safe Decision Delivery
Jingxiang Meng, Aofan Liu, Yaoshuai Ma, Fangxin Liu, Yongbiao Chen
Generative safety guards must classify requests correctly and return outputs that applications can parse. Reasoning guards can improve classification accuracy while producing outputs that applications cannot parse. When applications cannot extract a correct prediction, fallback handling can allow harmful requests or block safe ones. We introduce Resolve--Express--Act (REA), a framework that traces judgments through parsing and fallback to application actions. Experiments on classifiers and safety guards show that correct predictions can appear in incorrectly formatted outputs. Parsing the safety verdict preserves the reasoning guard's accuracy advantage, while stricter explanation requirements reject additional correct predictions. Accepting formatting variations recovers most rejected correct predictions. Held-out tests confirm that output requirements and generation limits affect whether correct predictions can be parsed. In our fail-open replay, matching the parser to the output and fixing formatting reduced the fraction of harmful requests allowed despite correct guard judgments from 95.5% to 0%. A fixed-output comparison shows that model-based extraction can preserve guard…
PDF arXiv

Papers

Topics in this field

coding agents · code generation · coding agent303 papers72 this week
skills · tasks · ai315 papers46 this week
recursive self improvement · harness evolution · adaptive271 papers41 this week
ai agents · indirect prompt injection · adversarial319 papers38 this week
tool using agents · agent systems · multi agent216 papers34 this week
agent · agentic · llm agents172 papers34 this week

Top tools here

All tools

QwenPaw+1266% downloads · 39k/wk+1266.0%
billion-context+259% downloads · 1.1M/wk+258.8%
jcode+233% downloads · 25.5k/wk+233.1%
oh-my-claudecode+47% downloads · 7.7k/wk+47.4%
TanStack AI+68% downloads · 1M/wk+67.7%

Activity · 12 weeks

Open problems in Agents

Long-horizon task completion with exploration and verification3 papers raise it · 2 address it
Agents struggle to accumulate evidence, adjust strategies, and verify results over long, realistic workflows.
- Scaling Agentic Data for Long-Horizon Terminal Intelligenceraises · addresses
- 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 Useraises
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 rather than just appearing to.
- AgentSWE: Can Coding Agents Build the Agent You Actually Want?raises · addresses
Realistic evaluation of agent learning from experience1 paper raises it · 1 addresses it
Existing benchmarks often test reasoning with known rules rather than learning novel, counterintuitive rules through interaction.
- 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
Agents cannot reliably identify when historical context is relevant, leading to missed or incorrect responses.
- RealCompanion: Benchmarking Human Understanding from Reasoning over Longitudinal Real-World Conversationsraises
Open-source, reproducible GUI agent stacks2 papers raise it · 2 address it
Frontier GUI 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 Environmentsraises · addresses

Named by an AI from these papers' abstracts.

Recently funded

Market

FirstWorkSeries A · Oct 21$15M
Asymptote LabsSeed · Oct 20$5.5M
LumilabPre-seed · Oct 20undisclosed
GreptileSeries B+ · Oct 19$50M
VesslaPre-seed · Oct 19$3.3M

Active contributors · 90 days

Recent papers

All papers in Research

arXiv cs.AIOct 13
When Should Enterprise Policy Decisions Be Learned, and When Should They Be Reasoned?
Hyojung Han, Ryangkyung Kang, Youngeun Kim, Seung-Hun Jeon
An enterprise agent that answers under a written policy must choose an action before it says anything: answer, ask for a missing fact, ask for confirmation, call one or several tools, escalate, or refuse. Holding one 27B Korean enterprise model fixed, we vary whether the decision is a generated JSON object or a single constrained token, and whether the model may reason first. On 2800 in-distribution items we did not detect an accuracy difference between the two deployed paths (93.46% vs 94.14%), while they differ about tenfold in restart stability (3.00% of actions flip vs 0.29%); they also differ in reasoning, so this is an operational comparison. On a sealed benchmark of 300 items whose labels come from a hidden executable policy, reasoning is worth +17.3 points [+12.3, +23.0] on exact action. With reasoning off, the format difference was 0.0 points [-4.3, +4.0], a failure to detect an advantage for either interface, not equivalence; with reasoning on it is unresolved. Against a token-matched random control, boundary-targeted supervision moves the two covered boundaries by +17.5 and +20.0 points and leaves a held-out boundary unchanged. In a 3-seed training x reasoning…
PDF arXiv
arXiv cs.AIOct 13
Executable Prefix Values for Locating Agent Failures
Bingyu Shen, Jiaqi Tang, Lan Wei, Yi Sheng, J. Jenny Li +1
Work on debugging LLM agents treats the "first mistake" marked by a human or LLM judge as ground truth for where a trajectory failed. We ask whether such labels point at causes or symptoms, and make the distinction executable: we restart the agent from reconstructed intermediate states of a failed trajectory and estimate its prefix value V(h_t), the probability of success from each step, with a certified point of no return (PNR) for a failure. On 150 SWE-bench Verified tasks with two frozen open-weight agents, we build such curves for 47 and 60 failed trajectories and probe every step labeled by four frontier judges under the Traverse rubric. We find that the labels do not mark what a controller needs. Judges of different lineages agree on the exact step in only 21/57 cases, and the newest judges flag mistakes in 8/10 and 10/10 successful runs. For 66% of the labels, the agent's success probability does not drop at the labeled step; only 7/188 answers name the decisive step. Restarting at the measured restart point raises expected success from 0.44 to 0.57 offline, and real restarts confirm the advantage, whereas restarting at a judge's label does not (-0.08 to -0.03). We release…
PDF arXiv
arXiv cs.SEOct 13
StateHarness: Maintaining Evolving Project State for Long-Horizon Coding Agents
Chuan Zou, Yujun Zhou, Xiaonan Luo, Xiangliang Zhang
Long-horizon coding requires agents to maintain an evolving view of the project they are changing: what problem is currently being solved, what has already been learned or modified, which other artifacts may be affected, and what work remains unresolved. When this information is scattered across a growing interaction history, local edits can become disconnected from their later consequences and progress can be difficult to carry forward. We introduce StateHarness, a runtime framework that maintains evolving project state without updating model parameters. The working agent records its current problem and hypothesis, important findings and decisions, discovered repository relationships, and unfinished work. Runtime support prompts record updates, searches for callers, tests, and documentation that may be affected by a change, and maintains a compact working brief for subsequent decisions. When behavioral signals accumulate, a separate diagnostic session revisits the recorded history and surfaces overlooked evidence. Across three repository-level evaluations, StateHarness improves task completion and several measures of change consistency. On RefactorBench, full-chain completion…
PDF arXiv
arXiv cs.AIOct 13
MAS-Gate: Unlocking Multi-Agent System Lifecycle via Node-Centric Quantification
Hehai Lin, Zixuan Wang, Ming Yan, Peidong Wang, Shilei Cao +4
Large language model (LLM)-based multi-agent systems (MAS) can solve complex problems beyond the reach of a single agent by decomposing tasks and coordinating complementary capabilities. Despite extensive research on MAS, existing work largely focuses on specific aspects or individual phases of their development and operation, resulting in fragmented perspectives with objectives and signals that are difficult to reconcile across these different settings. To bridge these disconnected perspectives, we conceptualize MAS development and operation as a three-phase lifecycle spanning design-time orchestration before execution, runtime intervention during execution, and post-hoc attribution after execution, and introduce MAS-Gate, a unified node-centric quantification applicable across these phases. Modeling each execution as a directed acyclic graph, MAS-Gate combines within-node utility, which estimates a node's contribution through changes in reference-answer likelihood, with inter-node strength, which approximates semantic uptake and potential downstream reach. We adapt this shared quantification to three objectives: providing a structure-aware dense reward for Orchestrator…
PDF arXiv
arXiv cs.AIOct 13
Verifier-Harness Co-Evolution for Training-Free Self-Improvement on Open-Ended Tasks
Tianyi Xu, Yaolun Zhang, Yiran Wu, Joe Nguyen, Shengyu Dai +4
Training-free self-improvement lets an agent use feedback to revise the response it is about to return and to update the harness state that shapes how it handles later tasks. On open-ended tasks that feedback rarely comes from reference answers or executable tests, so the agent has to rely on a model-based verifier. When the verifier is wrong, the current response can get worse, and misleading experience can persist across the task stream. A verifier that never changes also grows stale as the agent's responses improve and task demands shift. We introduce Unified Co-Evolution (UCoE), a training-free method that evolves an executable verifier program together with the agent's harness state while all model parameters stay fixed. UCoE synthesizes candidate verifier programs and tests them on response contrasts, pairs of responses that differ in one declared property and have a known preferred side. It selects the candidate whose discrimination on its weakest contrast family is highest, a quantity we call driving capacity. The selected verifier diagnoses one weakness in each draft, guides a single repair, decides which response is served, and determines whether the accepted repair may…
PDF arXiv
arXiv cs.AIOct 12
REA: From Correct LLM Judgments to Safe Decision Delivery
Jingxiang Meng, Aofan Liu, Yaoshuai Ma, Fangxin Liu, Yongbiao Chen
Generative safety guards must classify requests correctly and return outputs that applications can parse. Reasoning guards can improve classification accuracy while producing outputs that applications cannot parse. When applications cannot extract a correct prediction, fallback handling can allow harmful requests or block safe ones. We introduce Resolve--Express--Act (REA), a framework that traces judgments through parsing and fallback to application actions. Experiments on classifiers and safety guards show that correct predictions can appear in incorrectly formatted outputs. Parsing the safety verdict preserves the reasoning guard's accuracy advantage, while stricter explanation requirements reject additional correct predictions. Accepting formatting variations recovers most rejected correct predictions. Held-out tests confirm that output requirements and generation limits affect whether correct predictions can be parsed. In our fail-open replay, matching the parser to the output and fixing formatting reduced the fraction of harmful requests allowed despite correct guard judgments from 95.5% to 0%. A fixed-output comparison shows that model-based extraction can preserve guard…
PDF arXiv

Fields · AI · updated 1h ago

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