arXiv:2606. 24453v1 Announce Type: new Abstract: Modern coding agents pair LLM generators with various tools, including cheap diagnostics and expensive verifiers.
arXiv:2606. 30335v1 Announce Type: new Abstract: Autonomous scientific discovery systems increasingly use large language models (LLMs) to propose new hypotheses, but many such systems condition primarily on experimental memory: archives of high-scoring candidates or heuristic summaries of recent trials.
The paper introduces Speculative Uncertainty (SU), a technique that infers a failure likelihood for black‑box LLM agents by evaluating their generated token sequences with a lightweight draft model, without needing internal model details. SU extracts phase‑aware features from reasoning and action spans, calibrates them against verifiable outcomes, and produces a failure‑likelihood score usable by downstream policies. Applying a pre‑execution veto gate based on SU to software‑engineering agents such as Qwen3‑Coder‑480B and Claude 3.5 Sonnet reduced execution error rates by 6‑8 percentage points and token costs by 14‑19 %, while maintaining performance on out‑of‑distribution benchmarks and across different agent models.
arXiv:2608.22960v1 Announce Type: new Abstract: Coding agents are increasingly evaluated not only by whether they solve a task, but also by how they execute it. However, existing process-level evalua...
The paper introduces RuVerBench, a benchmark with 2,458 instances for evaluating the reliability of Large Language Models acting as judges (LaaJ) in verifying rubric compliance within agentic scenarios such as deep research and agentic coding. It systematically meta‑evaluates frontier LLMs, revealing that even the most advanced models perform well yet still produce substantial noise. The study also examines how prompt design, batching, and majority voting affect verification accuracy, noting that weaker models are more prompt‑sensitive, batched verification trades accuracy for efficiency, and majority voting offers diminishing returns.
arXiv:2606. 30850v1 Announce Type: new Abstract: Large language models (LLMs) are typically deployed in multi-turn conversations, where each turn provides new evidence that should reduce epistemic uncertainty about their environment.
arXiv:2607. 02873v1 Announce Type: cross Abstract: Large language model agents driving security tool suites over the Model Context Protocol are increasingly common.
arXiv:2609.16302v1 Announce Type: cross Abstract: When a coding agent returns to existing software, it inherits evidence from earlier engineering work: tests, type checks, proofs, static analyses, an...
Software-agent benchmarks usually report whether an agent solves a task, but the agent reaches that outcome through a harness that controls what it sees, which actions it can take, which failures are repaired, which states are verified, and which evidence is logged. We show that this harness can change the agent's multi-step beliefs even when the task, environment, and base LLM are fixed.
arXiv:2608. 11727v1 Announce Type: new Abstract: When a coding agent obeys a rule, it may simply have been going to do that anyway.
arXiv:2607. 25877v1 Announce Type: new Abstract: This paper investigates how multi-agent systems (MAS)-based on large language models (LLMs) can support actuarial risk modelling, with a particular focus on uncertainty quantification.
arXiv:2607. 05188v1 Announce Type: new Abstract: A coding agent solving a software-engineering task spends dozens of steps reasoning, editing code, and running tests, yet little is known about what the underlying language model internally represents about the program it is working on.
arXiv:2606. 07624v1 Announce Type: new Abstract: This discussion argues that sequential statistical inference can naturally contribute to LLM trustworthiness.