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Open problems in Agents

Long-horizon task execution and memory3 papers raise it · 3 address it
Agents fail to retain, retrieve, and use past information over long interactions, limiting real-world deployment.
- RealCompanion: Benchmarking Human Understanding from Reasoning over Longitudinal Real-World Conversationsraises
- Scaling Agentic Data for Long-Horizon Terminal Intelligenceraises · addresses
- Learn2Play Bench: How Well Do LLM Agents Learn from Experience in Unfamiliar Environments?raises · addresses
- Agent-Reflex: Teaching Language Agents to Act Reflectivelyaddresses
Real-world task ambiguity and unstated constraints3 papers raise it · 2 address it
Agents struggle with ambiguous, incomplete, or evolving requirements in live systems, unlike controlled benchmarks.
- OR-License: Evaluating LLM Agents as Real-World Operations Research Practitionersraises · addresses
- FDE-Gym: The Last Mile of AI Deliveryraises · addresses
- AgentSWE: Can Coding Agents Build the Agent You Actually Want?raises
Faithful agent development from requirements1 paper raises it · 1 addresses it
Coding agents often produce systems that appear correct but fail to meet the actual requirements.
- AgentSWE: Can Coding Agents Build the Agent You Actually Want?raises · addresses
Sequential distraction in multimodal GUI agents1 paper raises it · 1 addresses it
Omni-modal inputs cause agents to focus on irrelevant signals, leading to compounding errors.
- Focused Omni-Modal GUI Agentsraises · addresses
Open, reproducible agent stacks and evaluation1 paper raises it · 1 addresses it
Closed systems and lack of open benchmarks hinder systematic study and improvement of agent designs.
- OpenGCUA: Toward a Fully Open Stack for Generalist Computer Useraises · 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 papers89 this week
skills · tasks · ai315 papers61 this week
recursive self improvement · harness evolution · adaptive271 papers50 this week
tool using agents · agent systems · multi agent216 papers45 this week
ai agents · indirect prompt injection · adversarial319 papers43 this week
agent · agentic · llm agents172 papers42 this week

Topics in this field

Active contributors · 90 days

Active contributors

Activity · 12 weeks

Activity

Recent papers

All papers in Research

arXiv cs.SEOct 15
Plausible Is Not Enough: Trust and Evidence in AI-Authored Pull Request Review
Md Shamimur Rahman, Khairul Alam, Banani Roy, Chanchal K. Roy
Code review is where AI-authored pull requests (AIPRs) must earn reviewer trust before integration, yet it is unclear whether reviewers apply the same criteria used for human-authored pull requests (HPRs). We surveyed 239 practitioners from 31 countries using matched HPR and AIPR questions to compare trust, evidence requirements, and verification practices within respondents. Most respondents (64.9%) reported lower initial trust in AIPRs. They relied less on author track record and subsystem familiarity, emphasizing instead human prompter expertise and bounded change size. For approval, respondents more often considered their own reasoning through critical cases sufficient for AIPRs than HPRs (55.2% versus 28.5%), while relying less on test coverage. Overall, 61.1% reported higher evidence requirements for AIPRs, and the share who would block approval under residual uncertainty increased from 19.4% for HPRs to 43.9% for AIPRs. Respondents prioritized test inspection, project context, and local execution, and more often selected partial reimplementation to verify non-trivial logic. Unclear ownership was also more likely to pause AIPR review (29.3% versus 14.6%). Open-ended…
PDF arXiv
arXiv cs.HCOct 15
OnBodyAI - Why the Next AI Needs a Body
Michael Beigl
The next AI will not win by knowing more about the world. It will win because it knows you: because it lives on your body, works for you and answers to you. That is what the term OnBodyAI stands for. It describes what happens when large language models are given a body, and what research and industry need to make that happen. This article is based on my talk in the Industrial Panel at UbiComp/ISWC 2026 in Shanghai (October 13, 2026) and follows its ten key statements.
PDF arXiv
arXiv cs.CROct 15
Stronger Judges Do Not See More: Deployment-Integrity Blindness in LLM Evaluation
Jianchang Su, Dawei Xiang, Yifan Zhang, Wei Zhang
LLM-agent evaluation reports behavioral scores but leaves the identity of the deployed runtime stack unreported. A tampered deployment that preserves evaluated outputs passes every output-only evaluator. We introduce DIB-Bench, a benchmark that measures this deployment-integrity blindness across 7,560 scored examples, six tamper types, and an evaluator ladder from regex scorers through LLM judges to GPT-5-nano. Stronger evaluators accept more tampered outputs: mean acceptance rises from 88.6% (rule-based) to 94.1% (GPT-5-nano), and a trigger-conditional tamper invisible on the evaluation suite produces 48-100% harmful outputs, rising with model capability. A PAC-style bound shows that tampers with zero output shift are undetectable from outputs at any sample size; a manifest-hash identity channel detects them with zero samples. Behavioral fingerprinting identifies the model component above chance (76.7%) but covers one of six stack components. We also propose a graded deployment-identity descriptor for evaluation reports.
PDF arXiv
arXiv cs.PLOct 15
Normalization by Evaluation with Substitutions as Operations
Jason Z. S. Hu
In previous works on normalization by evaluation (NbE) with an untyped domain model, explicit substitutions are a major decision choice, which, in retrospect, is taken to simplify subsequent semantic proofs. This choice hence is taken for a purely technical reason. In addition, there is no obvious lemma to test the completeness of the definition or the laws for explicit substitutions. In this technical report, we investigate NbE in a typical setting, i.e. defining substitutions as operations. We show that this setting brings simplifications in proving syntactic properties and soundness of NbE, though complications are introduced in the completeness proof of NbE.
PDF arXiv
arXiv cs.AIOct 15
Sensitivity Analysis of Decision Rules in an Agent-Based Agro-Ecological Model
Benjamin Pianet, Baptiste Lesquoy, Paul Saves, Benoit Gaudou, Jean Villerd +2
Agent-based models are well-suited for modelling agro-ecological systems for decision-making, yet turning their simulations into actionable insight requires identifying which decisions actually drive the behaviours. In such a context, sensitivity analysis stumbles upon a structural feature of many complex systems: the parameters are not a flat vector but a decision tree in which many parameters appear only when a higher-level choice activates them. Classical global sensitivity analysis presupposes independent inputs over a fixed Cartesian product and is ill-posed on such a branched space and biased by construction. We instead treat the structure of the decisions as a first-class modelling object. Using advanced surrogate modelling techniques, we encode the crop-management itinerary of the high-resolution agro-ecological modelling platform MAELIA as a hierarchical design space featuring both categorical and numerical variables. We propose a novel dedicated method to analyse this kind of hierarchical space, a random-forest-supervised HSIC-ANOVA, which ranks main effects and interactions natively on the tree while placing rarely and always active decisions on an equal footing. We…
PDF arXiv
arXiv cs.MAOct 15
SSR: Structural Signal Retriever for Scalable Agent Discovery in LLM-Orchestrated Multi-Agent Systems
Giancarlo Maldonado Cárdenas, Carlos A. Astudillo, Nelson L. S. da Fonseca
Large-scale multi-agent ecosystems require mechanisms for discovering suitable agents without exposing entire registries to a large language model (LLM) orchestrator. The challenge is not only to retrieve relevant Agent Cards from a growing registry, but also to maintain a compact candidate context, since each additional candidate increases prompt size, inference cost, latency, and the risk of exceeding the model context window. We formulate this problem as candidate-context scalability and propose the Structural Signal Retriever (SSR), our deterministic and training-free retrieval method designed for Agent Card discovery. SSR derives structural lexical signals from capability-bearing Agent Card fields, weights them using inverse document frequency (IDF), and uses an Inverted Signal Index (ISI) for selective candidate scoring. We evaluate SSR using a synthetic agent benchmark, a public MS MARCO adaptation, and end-to-end LLM orchestration. At 1,000 agents, SSR reduces LLM input by 98.8% and orchestration latency by 70.9% relative to full-catalog orchestration. On MS MARCO, IDF improves retrieval effectiveness within the structural representation, while the ISI preserves retrieval…
PDF arXiv

Papers

Top tools here

All tools

Agno5 releases this week
FastMCP6 releases this week
Pydantic AI+17% downloads · 1.5M/wk+17.0%
n8n+25% downloads · 148.3k/wk+25.0%
smolagents+19% downloads · 125.9k/wk+18.7%

Topics in this field

coding agents · code generation · coding agent303 papers89 this week
skills · tasks · ai315 papers61 this week
recursive self improvement · harness evolution · adaptive271 papers50 this week
tool using agents · agent systems · multi agent216 papers45 this week
ai agents · indirect prompt injection · adversarial319 papers43 this week
agent · agentic · llm agents172 papers42 this week

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

Recent papers

All papers in Research

arXiv cs.SEOct 15
Plausible Is Not Enough: Trust and Evidence in AI-Authored Pull Request Review
Md Shamimur Rahman, Khairul Alam, Banani Roy, Chanchal K. Roy
Code review is where AI-authored pull requests (AIPRs) must earn reviewer trust before integration, yet it is unclear whether reviewers apply the same criteria used for human-authored pull requests (HPRs). We surveyed 239 practitioners from 31 countries using matched HPR and AIPR questions to compare trust, evidence requirements, and verification practices within respondents. Most respondents (64.9%) reported lower initial trust in AIPRs. They relied less on author track record and subsystem familiarity, emphasizing instead human prompter expertise and bounded change size. For approval, respondents more often considered their own reasoning through critical cases sufficient for AIPRs than HPRs (55.2% versus 28.5%), while relying less on test coverage. Overall, 61.1% reported higher evidence requirements for AIPRs, and the share who would block approval under residual uncertainty increased from 19.4% for HPRs to 43.9% for AIPRs. Respondents prioritized test inspection, project context, and local execution, and more often selected partial reimplementation to verify non-trivial logic. Unclear ownership was also more likely to pause AIPR review (29.3% versus 14.6%). Open-ended…
PDF arXiv
arXiv cs.HCOct 15
OnBodyAI - Why the Next AI Needs a Body
Michael Beigl
The next AI will not win by knowing more about the world. It will win because it knows you: because it lives on your body, works for you and answers to you. That is what the term OnBodyAI stands for. It describes what happens when large language models are given a body, and what research and industry need to make that happen. This article is based on my talk in the Industrial Panel at UbiComp/ISWC 2026 in Shanghai (October 13, 2026) and follows its ten key statements.
PDF arXiv
arXiv cs.CROct 15
Stronger Judges Do Not See More: Deployment-Integrity Blindness in LLM Evaluation
Jianchang Su, Dawei Xiang, Yifan Zhang, Wei Zhang
LLM-agent evaluation reports behavioral scores but leaves the identity of the deployed runtime stack unreported. A tampered deployment that preserves evaluated outputs passes every output-only evaluator. We introduce DIB-Bench, a benchmark that measures this deployment-integrity blindness across 7,560 scored examples, six tamper types, and an evaluator ladder from regex scorers through LLM judges to GPT-5-nano. Stronger evaluators accept more tampered outputs: mean acceptance rises from 88.6% (rule-based) to 94.1% (GPT-5-nano), and a trigger-conditional tamper invisible on the evaluation suite produces 48-100% harmful outputs, rising with model capability. A PAC-style bound shows that tampers with zero output shift are undetectable from outputs at any sample size; a manifest-hash identity channel detects them with zero samples. Behavioral fingerprinting identifies the model component above chance (76.7%) but covers one of six stack components. We also propose a graded deployment-identity descriptor for evaluation reports.
PDF arXiv
arXiv cs.PLOct 15
Normalization by Evaluation with Substitutions as Operations
Jason Z. S. Hu
In previous works on normalization by evaluation (NbE) with an untyped domain model, explicit substitutions are a major decision choice, which, in retrospect, is taken to simplify subsequent semantic proofs. This choice hence is taken for a purely technical reason. In addition, there is no obvious lemma to test the completeness of the definition or the laws for explicit substitutions. In this technical report, we investigate NbE in a typical setting, i.e. defining substitutions as operations. We show that this setting brings simplifications in proving syntactic properties and soundness of NbE, though complications are introduced in the completeness proof of NbE.
PDF arXiv
arXiv cs.AIOct 15
Sensitivity Analysis of Decision Rules in an Agent-Based Agro-Ecological Model
Benjamin Pianet, Baptiste Lesquoy, Paul Saves, Benoit Gaudou, Jean Villerd +2
Agent-based models are well-suited for modelling agro-ecological systems for decision-making, yet turning their simulations into actionable insight requires identifying which decisions actually drive the behaviours. In such a context, sensitivity analysis stumbles upon a structural feature of many complex systems: the parameters are not a flat vector but a decision tree in which many parameters appear only when a higher-level choice activates them. Classical global sensitivity analysis presupposes independent inputs over a fixed Cartesian product and is ill-posed on such a branched space and biased by construction. We instead treat the structure of the decisions as a first-class modelling object. Using advanced surrogate modelling techniques, we encode the crop-management itinerary of the high-resolution agro-ecological modelling platform MAELIA as a hierarchical design space featuring both categorical and numerical variables. We propose a novel dedicated method to analyse this kind of hierarchical space, a random-forest-supervised HSIC-ANOVA, which ranks main effects and interactions natively on the tree while placing rarely and always active decisions on an equal footing. We…
PDF arXiv
arXiv cs.MAOct 15
SSR: Structural Signal Retriever for Scalable Agent Discovery in LLM-Orchestrated Multi-Agent Systems
Giancarlo Maldonado Cárdenas, Carlos A. Astudillo, Nelson L. S. da Fonseca
Large-scale multi-agent ecosystems require mechanisms for discovering suitable agents without exposing entire registries to a large language model (LLM) orchestrator. The challenge is not only to retrieve relevant Agent Cards from a growing registry, but also to maintain a compact candidate context, since each additional candidate increases prompt size, inference cost, latency, and the risk of exceeding the model context window. We formulate this problem as candidate-context scalability and propose the Structural Signal Retriever (SSR), our deterministic and training-free retrieval method designed for Agent Card discovery. SSR derives structural lexical signals from capability-bearing Agent Card fields, weights them using inverse document frequency (IDF), and uses an Inverted Signal Index (ISI) for selective candidate scoring. We evaluate SSR using a synthetic agent benchmark, a public MS MARCO adaptation, and end-to-end LLM orchestration. At 1,000 agents, SSR reduces LLM input by 98.8% and orchestration latency by 70.9% relative to full-catalog orchestration. On MS MARCO, IDF improves retrieval effectiveness within the structural representation, while the ISI preserves retrieval…
PDF arXiv

Active contributors · 90 days

Activity · 12 weeks

Open problems in Agents

Long-horizon task execution and memory3 papers raise it · 3 address it
Agents fail to retain, retrieve, and use past information over long interactions, limiting real-world deployment.
- RealCompanion: Benchmarking Human Understanding from Reasoning over Longitudinal Real-World Conversationsraises
- Scaling Agentic Data for Long-Horizon Terminal Intelligenceraises · addresses
- Learn2Play Bench: How Well Do LLM Agents Learn from Experience in Unfamiliar Environments?raises · addresses
- Agent-Reflex: Teaching Language Agents to Act Reflectivelyaddresses
Real-world task ambiguity and unstated constraints3 papers raise it · 2 address it
Agents struggle with ambiguous, incomplete, or evolving requirements in live systems, unlike controlled benchmarks.
- OR-License: Evaluating LLM Agents as Real-World Operations Research Practitionersraises · addresses
- FDE-Gym: The Last Mile of AI Deliveryraises · addresses
- AgentSWE: Can Coding Agents Build the Agent You Actually Want?raises
Faithful agent development from requirements1 paper raises it · 1 addresses it
Coding agents often produce systems that appear correct but fail to meet the actual requirements.
- AgentSWE: Can Coding Agents Build the Agent You Actually Want?raises · addresses
Sequential distraction in multimodal GUI agents1 paper raises it · 1 addresses it
Omni-modal inputs cause agents to focus on irrelevant signals, leading to compounding errors.
- Focused Omni-Modal GUI Agentsraises · addresses
Open, reproducible agent stacks and evaluation1 paper raises it · 1 addresses it
Closed systems and lack of open benchmarks hinder systematic study and improvement of agent designs.
- OpenGCUA: Toward a Fully Open Stack for Generalist Computer Useraises · addresses

Named by an AI from these papers' abstracts.

Fields · AI · updated 1h ago

Agents

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

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