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Agents

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

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arXiv cs.CLCodeOct 16▲ 18
DiscoveryRSI: Recursively Learning to Discover Valuable Hypotheses
Jianyuan Zhong, Chenwei Wu, Zhaochen Yu, Xixian Jiang, Xuewei Li +19
Scientific discovery requires developing worthwhile hypotheses into practical investigations, yet learning from this evolving research process remains challenging. We introduce an operational definition of valuable and testable research hypotheses: evidence-grounded claims about consequential uncertainties, coupled with feasible investigations, explicit decision criteria, and alternative explanations. Our DiscoveryRSI framework develops hypothesis trees through multi-agent evolutionary search, compiles selected investigations into executable Harbor tasks, and recursively learns through three mechanisms: hypothesis credit optimization attributes scientific assessments to documented research contributions; harness replay identifies productive strategies and recurring failures to refine research workflows; and rediscovery applies accepted improvements to new investigations, generating further experience for subsequent learning. We also introduce DFM-Bench, which provides comprehensive assessment of hypothesis quality and cumulative research progress at the levels of hypotheses, revisions, development paths and research runs. Across four life-science domains, automated evaluation…
PDF arXiv Code
arXiv cs.AIOct 16
Self-Building Labs
Jiaru Bai, Yuchi Zhao, Han Hao, Miroslav Bogdanovic, Yang Cao +2
Self-driving laboratories (SDLs) accelerate scientific discovery by automating experimental execution and optimization. AI agents can increasingly propose new experiments, but existing SDLs can only run the procedures they were built for. Adapting them to a new experiment still takes extensive manual engineering, which makes laboratory flexibility the bottleneck for scaling discovery. We introduce the self-building laboratory (SBL), which designs, configures, and operates its own laboratory setup given a high-level scientific goal. SBL starts from whatever already exists, from an empty room to a working SDL, and builds the missing capabilities: the lab layout, its digital twin, robot skills, and experimental workflows. We demonstrate the system working across different robots and setups, scaling from a single workstation to laboratories with several robots working together. We validate SBL's end-to-end capabilities through a real-world quantum-dot synthesis experiment. By shifting the role of human researchers from manually configuring and programming device automation to specifying scientific objectives, SBL makes scientific automation more accessible, flexible, and scalable.…
PDF arXiv
arXiv cs.SEOct 16
Enforcing Code Reasoning in Agentic Scaffold for More Effective Issue Resolution
Changshu Liu, Ji young Kim, Alireza Ghazanfari, Saanvi Nallu, Reyhaneh Jabbarvand
Reasoning is central to ReAct-based programming agents, yet whether and how they reason about code remains unclear. We study 7,955 issue-resolution trajectories from six single- and multi-agent scaffolds across four programming languages on SWE-bench-Verified, SWE-bench-Pro, and SWE-bench-Lite. We identify 12 forms of code reasoning in 66.95% of trajectories. Code reasoning is associated with higher resolution rates, and its intensity increases with task complexity. However, it remains largely unguided: agents may focus on surface-level symptoms during localization, fail to refine near-correct patches, overlook issue requirements, and generate tests with limited behavioral diversity. To address these limitations, we propose DeskCheck, an agentic scaffold that systematically guides code reasoning throughout repository-level issue resolution. DeskCheck simulates execution paths to localize root causes, simulates candidate patches to refine near-misses, audits patches against issue requirements, and reasons about corner-case inputs and expected outputs before generating tests. On 731 SWE-bench-Pro instances, DeskCheck improves resolution rates over mini-SWE-Agent and Claude Code by…
PDF arXiv
arXiv cs.CROct 16
ToolRosetta: Surface-Form Invariance of Tool-Call Guards
Hasan Demirkiran, Jens Ernstberger
Step-level guards for tool-using agents are evaluated on tidy JSON tool calls, but deployed coding agents act through execute_bash and execute_code tools whose payloads are strings. We introduce ToolRosetta, which re-renders every candidate action in the AgentDojo and AgentHarm splits of TS-Bench as a Python call, as a shell command, and without the agent's rationale. The renderings are produced by code and verified by round-trip parsing, so the user request, the history, the label and the action's effect stay fixed while only the surface form changes. Across eleven guards with two placebo controls, frontier and strong open-weight guards are stable: no frontier error rate moves by more than 3.4 points. Four of five specialized agent guards are not: under the shell form their false-positive rates rise by 21 to 47 points on AgentDojo, and TS-Guard's by 38 points on its own home benchmark, blocking between a third and nine in ten benign actions; the fifth, StepGuard, does not move. A small open guard drifts five points more permissive. Removing the agent's rationale moves most guards whose input carries it, in guard-specific directions. A request-blind control on one guard shows…
PDF arXiv
arXiv cs.LOOct 16
LLM-Assisted Bi-Approximate CHC Solving
Baiting Jiang
Constrained Horn clause (CHC) solvers often struggle to discover deep counterexamples whose derivations may require extensive rule unfolding. Although large language models (LLMs) can propose long-range symbolic relations, prior work on formally checked LLM-assisted verification has mainly focused on generating inductive invariants and concrete traces. We exploit this broader capability through Derivation Power Under-approximations (DPUs). Each DPU is a solver-certified under-approximation of the relation induced by a multi-step CHC derivation. An LLM proposes each DPU with symbolic intermediate-state witnesses that enable SMT validation without introducing additional quantifiers. Validated DPUs can be composed and reused soundly. Building on this mechanism, our LLM-assisted framework HornCraft combines invariant generation for over-approximation with DPU generation for under-approximation in CHC solving. On 260 linear and nonlinear integer-arithmetic CHC-COMP benchmarks left unresolved by a 30-minute Spacer run, HornCraft returns certified verdicts for 228 instances. It certifies 42 instances as unsafe, compared with 22 when DPU construction and reuse are disabled.
PDF arXiv
arXiv cs.CROct 16
Red-Teaming Multi-Agent Attacks for Loss-of-Control Risks
John Kitaoka, Thao Amelia Pham, Carlos Rafael Giudice, Jacob Merizian
AI agents are growing more capable and persistent, and are being deployed in ever larger numbers. As more start collaborating, AI control measures must remain effective when they covertly collude on a harmful objective. We introduce MiniLab, a synthetic series of live services and codebases (5 interconnected services, 249 benign tasks, 8 attack objectives), adapted from internal AI deployment infrastructure. We demonstrate distributed attacks, in which different agents carry out separate attack fragments, and how their covertness evades frontier monitors when reading their trajectories individually. We show how distributed attacks spread across diverse contexts and harmful objectives through 16 human-optimized attacks (weight exfiltration, unmonitored rogue deployment, permission elevation, and more). A frontier monitor, GPT-5.6 Sol, reading each agent separately flags only 7% of successful distributed attacks, compared with 87% when a single agent carries out the same side task. Individual attacks can also be composed into longer attack sequences toward a greater loss-of-control outcome, without any individual agent being flagged. Finally, we tested whether models can plan these…
PDF arXiv

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arXiv cs.CLCodeOct 16▲ 18
DiscoveryRSI: Recursively Learning to Discover Valuable Hypotheses
Jianyuan Zhong, Chenwei Wu, Zhaochen Yu, Xixian Jiang, Xuewei Li +19
Scientific discovery requires developing worthwhile hypotheses into practical investigations, yet learning from this evolving research process remains challenging. We introduce an operational definition of valuable and testable research hypotheses: evidence-grounded claims about consequential uncertainties, coupled with feasible investigations, explicit decision criteria, and alternative explanations. Our DiscoveryRSI framework develops hypothesis trees through multi-agent evolutionary search, compiles selected investigations into executable Harbor tasks, and recursively learns through three mechanisms: hypothesis credit optimization attributes scientific assessments to documented research contributions; harness replay identifies productive strategies and recurring failures to refine research workflows; and rediscovery applies accepted improvements to new investigations, generating further experience for subsequent learning. We also introduce DFM-Bench, which provides comprehensive assessment of hypothesis quality and cumulative research progress at the levels of hypotheses, revisions, development paths and research runs. Across four life-science domains, automated evaluation…
PDF arXiv Code
arXiv cs.AIOct 16
Self-Building Labs
Jiaru Bai, Yuchi Zhao, Han Hao, Miroslav Bogdanovic, Yang Cao +2
Self-driving laboratories (SDLs) accelerate scientific discovery by automating experimental execution and optimization. AI agents can increasingly propose new experiments, but existing SDLs can only run the procedures they were built for. Adapting them to a new experiment still takes extensive manual engineering, which makes laboratory flexibility the bottleneck for scaling discovery. We introduce the self-building laboratory (SBL), which designs, configures, and operates its own laboratory setup given a high-level scientific goal. SBL starts from whatever already exists, from an empty room to a working SDL, and builds the missing capabilities: the lab layout, its digital twin, robot skills, and experimental workflows. We demonstrate the system working across different robots and setups, scaling from a single workstation to laboratories with several robots working together. We validate SBL's end-to-end capabilities through a real-world quantum-dot synthesis experiment. By shifting the role of human researchers from manually configuring and programming device automation to specifying scientific objectives, SBL makes scientific automation more accessible, flexible, and scalable.…
PDF arXiv
arXiv cs.SEOct 16
Enforcing Code Reasoning in Agentic Scaffold for More Effective Issue Resolution
Changshu Liu, Ji young Kim, Alireza Ghazanfari, Saanvi Nallu, Reyhaneh Jabbarvand
Reasoning is central to ReAct-based programming agents, yet whether and how they reason about code remains unclear. We study 7,955 issue-resolution trajectories from six single- and multi-agent scaffolds across four programming languages on SWE-bench-Verified, SWE-bench-Pro, and SWE-bench-Lite. We identify 12 forms of code reasoning in 66.95% of trajectories. Code reasoning is associated with higher resolution rates, and its intensity increases with task complexity. However, it remains largely unguided: agents may focus on surface-level symptoms during localization, fail to refine near-correct patches, overlook issue requirements, and generate tests with limited behavioral diversity. To address these limitations, we propose DeskCheck, an agentic scaffold that systematically guides code reasoning throughout repository-level issue resolution. DeskCheck simulates execution paths to localize root causes, simulates candidate patches to refine near-misses, audits patches against issue requirements, and reasons about corner-case inputs and expected outputs before generating tests. On 731 SWE-bench-Pro instances, DeskCheck improves resolution rates over mini-SWE-Agent and Claude Code by…
PDF arXiv
arXiv cs.CROct 16
ToolRosetta: Surface-Form Invariance of Tool-Call Guards
Hasan Demirkiran, Jens Ernstberger
Step-level guards for tool-using agents are evaluated on tidy JSON tool calls, but deployed coding agents act through execute_bash and execute_code tools whose payloads are strings. We introduce ToolRosetta, which re-renders every candidate action in the AgentDojo and AgentHarm splits of TS-Bench as a Python call, as a shell command, and without the agent's rationale. The renderings are produced by code and verified by round-trip parsing, so the user request, the history, the label and the action's effect stay fixed while only the surface form changes. Across eleven guards with two placebo controls, frontier and strong open-weight guards are stable: no frontier error rate moves by more than 3.4 points. Four of five specialized agent guards are not: under the shell form their false-positive rates rise by 21 to 47 points on AgentDojo, and TS-Guard's by 38 points on its own home benchmark, blocking between a third and nine in ten benign actions; the fifth, StepGuard, does not move. A small open guard drifts five points more permissive. Removing the agent's rationale moves most guards whose input carries it, in guard-specific directions. A request-blind control on one guard shows…
PDF arXiv
arXiv cs.LOOct 16
LLM-Assisted Bi-Approximate CHC Solving
Baiting Jiang
Constrained Horn clause (CHC) solvers often struggle to discover deep counterexamples whose derivations may require extensive rule unfolding. Although large language models (LLMs) can propose long-range symbolic relations, prior work on formally checked LLM-assisted verification has mainly focused on generating inductive invariants and concrete traces. We exploit this broader capability through Derivation Power Under-approximations (DPUs). Each DPU is a solver-certified under-approximation of the relation induced by a multi-step CHC derivation. An LLM proposes each DPU with symbolic intermediate-state witnesses that enable SMT validation without introducing additional quantifiers. Validated DPUs can be composed and reused soundly. Building on this mechanism, our LLM-assisted framework HornCraft combines invariant generation for over-approximation with DPU generation for under-approximation in CHC solving. On 260 linear and nonlinear integer-arithmetic CHC-COMP benchmarks left unresolved by a 30-minute Spacer run, HornCraft returns certified verdicts for 228 instances. It certifies 42 instances as unsafe, compared with 22 when DPU construction and reuse are disabled.
PDF arXiv
arXiv cs.CROct 16
Red-Teaming Multi-Agent Attacks for Loss-of-Control Risks
John Kitaoka, Thao Amelia Pham, Carlos Rafael Giudice, Jacob Merizian
AI agents are growing more capable and persistent, and are being deployed in ever larger numbers. As more start collaborating, AI control measures must remain effective when they covertly collude on a harmful objective. We introduce MiniLab, a synthetic series of live services and codebases (5 interconnected services, 249 benign tasks, 8 attack objectives), adapted from internal AI deployment infrastructure. We demonstrate distributed attacks, in which different agents carry out separate attack fragments, and how their covertness evades frontier monitors when reading their trajectories individually. We show how distributed attacks spread across diverse contexts and harmful objectives through 16 human-optimized attacks (weight exfiltration, unmonitored rogue deployment, permission elevation, and more). A frontier monitor, GPT-5.6 Sol, reading each agent separately flags only 7% of successful distributed attacks, compared with 87% when a single agent carries out the same side task. Individual attacks can also be composed into longer attack sequences toward a greater loss-of-control outcome, without any individual agent being flagged. Finally, we tested whether models can plan these…
PDF arXiv

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Agents

Recent papers

DiscoveryRSI: Recursively Learning to Discover Valuable Hypotheses

Self-Building Labs

Enforcing Code Reasoning in Agentic Scaffold for More Effective Issue Resolution

ToolRosetta: Surface-Form Invariance of Tool-Call Guards

LLM-Assisted Bi-Approximate CHC Solving

Red-Teaming Multi-Agent Attacks for Loss-of-Control Risks

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Recent papers

DiscoveryRSI: Recursively Learning to Discover Valuable Hypotheses

Self-Building Labs

Enforcing Code Reasoning in Agentic Scaffold for More Effective Issue Resolution

ToolRosetta: Surface-Form Invariance of Tool-Call Guards

LLM-Assisted Bi-Approximate CHC Solving

Red-Teaming Multi-Agent Attacks for Loss-of-Control Risks