Structural certification framework for general agents in the big-world regime
A new arXiv preprint introduces 'structural certification,' a transition-local framework for formally bounding the reliability of general agents in environments too large for universal competence. The authors prove that general agents cannot be universal, making standard worst-case guarantees uninformative, and then provide algorithms that filter transitions via deep compositional goals to produce entry-wise error bounds of O(1/n) + O(δ) on an agent's internal world model. The work aims to enable certifiable deployment of general agents by identifying specific transitions where long-horizon planning is provably reliable.
Related guides (2)
Related events (8)
Distributionally robust optimization framework for probabilistic runtime verification of AI agents
A new arXiv preprint introduces a sound and efficient framework for verifying probabilistic security policies for AI agents operating in complex digital environments, addressing limitations of prior Datalog-based approaches that assumed deterministic policies or predicate independence. The method uses distributionally robust optimization to compute sound upper bounds on policy violation probability without requiring independence assumptions between predicates. Evaluated on benchmarks for terminal and tool-calling agents, the approach outperforms prior art on the security-utility trade-off.
Goedel-Architect achieves state-of-the-art formal theorem proving with blueprint-based agentic framework
Goedel-Architect is an agentic framework for formal theorem proving in Lean 4 that uses blueprint generation — a dependency graph of definitions and lemmas — rather than recursive decomposition, enabling parallel lemma closure and global refinement. Built on DeepSeek-V4-Flash (284B-A13B), it achieves 99.2% pass@1 on MiniF2F-test and 75.6% on PutnamBench, scaling to 100% on MiniF2F, 88.8% on PutnamBench, and 4/6 on IMO 2025 when seeded with natural-language proofs. The authors claim state-of-the-art performance for an open-source pipeline at up to 500x lower cost than comparable systems.
Unfireable Safety Kernel: Formal execution-time alignment layer for escapable AI agents
A new arXiv preprint introduces the concept of 'escapable AI systems' — agents with sufficient reach into their own runtime to subvert in-process safety controls — and proposes a four-property architectural framework for external enforcement. The authors present the Unfireable Safety Kernel, a Rust reference implementation with machine-checked fail-closed invariants via SMT (Z3) and bounded model checking (Kani), evaluated against a self-improving world model adversary across 7,240 authorization attempts with zero successful bypasses. The work positions this 'execution-time alignment' layer as a complement to training-time approaches like RLHF and Constitutional AI, arguing that any control inside the agent's address space is fundamentally reachable by adversarial inputs.
Framework for quantifying faithful confidence expression in large reasoning models
A new arXiv preprint introduces a framework to measure faithful calibration (FC) in large reasoning models (LRMs)—the alignment between a model's intrinsic confidence and its linguistically expressed confidence. The authors analyze linguistic decisiveness against three internal uncertainty sources (token probabilities, hidden states, sampled response consistency) and introduce prefix-conditioned sampling to handle structural variation in chain-of-thought traces. Applying the framework across leading models, they find FC is a significant and distinct failure mode for LRMs: extended reasoning traces do not automatically improve calibration, prompt interventions that help non-reasoning models fail in the reasoning setting, and different confidence estimators produce divergent assessments of the same traces.
Bounding Compositional Incoherence in Multi-Component LLM Agents
This paper formalizes a failure mode in multi-component LLM agent systems where individual components are locally probabilistically coherent but their composition violates basic probability axioms. The authors introduce the 'compositional residual' (eps*) as a runtime-computable measure of this incoherence, finding it positive in 33–94% of ensemble cliques across 1,876 tested configurations on a four-LLM panel. A hierarchical Boyle-Dykstra projection is proposed as a deterministic repair, and an anytime-valid e-process enables sequential monitoring. Notably, three intuitive LLM-side mitigations—retrieval, partition-aware prompting, and aggregator-LLM—each fail or regress.
New Paper: Towards a Science of AI Agent Reliability
A new paper proposes a framework for quantifying the gap between AI agent capability and reliability, aiming to establish a more rigorous science of agent dependability. The work addresses the observation that agents may demonstrate high capability on benchmarks while failing unpredictably in deployment. The piece is published via the normaltech.ai newsletter, associated with the AI Snake Oil research commentary tradition.
Anthropic publishes framework for safe and trustworthy agent development
Anthropic released a formal framework for responsible agent development, articulating principles around human oversight, transparency, value alignment, and privacy for autonomous AI agents. The document draws on Claude Code as a reference implementation and cites enterprise deployments at Trellix and Block as real-world examples. The framework is positioned as a contribution to emerging industry standards for agentic AI systems, acknowledging open technical challenges in value alignment measurement and oversight calibration.
AgentSpec: A modular framework for controlled composition and analysis of embodied LLM agent scaffolds
AgentSpec is a new modular specification framework that represents embodied LLM agents as typed compositions of reusable policy components with standardized interfaces across perception, memory, reasoning, reflection, action, and learning modules. The framework enables controlled swapping and recombination of components, instantiated across four benchmarks (DeliveryBench, ALFRED, MiniGrid, RoboTHOR). Key findings include that agent performance is governed by scaffold compatibility and interaction effects rather than isolated module strength, and that RL-trained policies compose best when optimized with deployment-time scaffold structure. Code, baselines, and an interactive playground are publicly released.