Chain-of-thought (CoT) prompting makes language models write step-by-step explanations, but these steps may not match what the model actually used to choose its answer. Existing faithfulness checks often only test whether changing the written chain changes the answer, without verifying whether the steps are truly supported by the given evidence, or they require special prompts that do not generalize well. We present NSF-CoT, a neuro-symbolic formal verification method that checks CoT faithfulness step by step for contextual question answering. NSF-CoT (1) converts the provided context facts and each reasoning step into simple logical statements, (2) uses counterfactual attribution to estimate which context facts the model relied on while generating each step, and (3) verifies each step using a hybrid checker that combines an SMT solver with an LLM-based entailment judge. For every step, we score groundedness (supported by the full context), validity (supported by the facts the model relied on), and utility (helps reach the final answer), and combine them into a faithfulness score. Across OpenBookQA, QASC, and HotpotQA, NSF-CoT consistently outperforms causal mediation, perturbation probes, and behavioral monitoring, and it identifies reasoning steps that are not only unfaithful but also harmful to the model’s final decision.

Text-to-image diffusion models achieve remarkable generation quality, yet their internal mechanisms for grounding prompt semantics into visual structure remain poorly understood. We present a novel mechanistic interpretability framework for Stable Diffusion that probes how individual prompt tokens are represented and utilized during the denoising process. Given a prompt, we record cross-attention activations throughout UNet denoising and convert them into token-level spatial grounding maps that indicate where each token contributes signal during image synthesis. To establish causal faithfulness, we perform controlled prompt interventions by removing a single word at a time while keeping the sampling seed fixed, producing counterfactual generations. To quantify mechanistic sensitivity, we introduce a head-resolved spike score based on divergence between per-head token contribution distributions before and after intervention, enabling module-wise and head-wise attribution of semantic changes. Experiments on compositional prompts and challenging relational descriptions reveal systematic patterns of token grounding, semantic drift, and head specialization across denoising timesteps. Our results provide a practical and reproducible toolkit for analyzing how diffusion models encode and apply semantic information, supporting deeper transparency in text-to-image generation.

Multi-agent LLM systems assign different responsibilities to different agents: one plans, another retrieves evidence, another verifies, another executes. This division of labor is only meaningful if each agent stays within the functions it is authorized to perform. Over many turns, however, an agent can produce a locally reasonable message that exercises authority belonging to another role: a verifier that begins planning, a retriever that asserts unsupported conclusions, an executor that changes state no one approved. We formalize this as role drift, a turn-level breach of an agent’s role contract. We introduce Agent Trajectory Control (ATC), which makes each role’s authority explicit as a contract and enforces it at the point where a message would enter the shared conversation. ATC scores a proposed output for out-of-role deviation, leakage into the functions of other roles, impermissible speech acts, and logical contract breaches, then computes a leakage-regularized target that constrains regeneration; the revised message is re-checked before admission. Enforcement acts on the message rather than the generator, so ATC governs agents whose internal representations are unavailable. Across homogeneous and heterogeneous teams, ATC improves task performance and role consistency while lowering role-drift energy relative to strong baselines spanning prompting, memory, workflow control, and activation editing, with the largest gains under explicit role-confusion stress tests and in larger, more diverse teams.

Long-horizon multi-agent systems suffer from three tightly coupled failures: task drift, role drift, and state staleness. We test the hypothesis that these share a common representational cause: agent hidden states move away from the directions encoding the active task, assigned role, and current shared state. A pilot study over 14,832 annotated turns supports this: drifted turns collapse near-uniformly across all three anchor subspaces (pairwise r = 0.72–0.78). Existing methods address these failures separately, operating at the surface-text level or treating control directions independently. We introduce ATLAS (Algebraic Task–role Locking with Adaptive Spectral Memory), a training-free intervention acting on hidden states before a token is generated. ATLAS builds a positive-semidefinite anchor kernel over task, role, and state subspaces and steers hidden states toward their shared high-coherence eigendirections; a low-rank Gram decomposition keeps this tractable. Across long-horizon memory, state maintenance, tool use, least-privilege coordination, and embodied stress tests, ATLAS outperforms prompting, retrieval, memory, and activation-steering baselines. Against A-MEM it improves memory macro-average accuracy by up to 5.7 points and reduces write/read failures by up to 31.7%/24.8%; against MemoryArena-Agent it gains up to 4.8 points. It gains 10.7–12.1 points under perturbation, reaches 93.5% role consistency, and transfers to a 72B backbone (macro-average 61.0 vs. 56.2). Ablating the joint task–role–state span costs 7.2 points against 0.8 for a matched-rank random span, indicating these directions are functionally implicated, not incidental; supplying them by prompt or retrieval recovers 4.3 points less. ATLAS is therefore a representation-level controller for open-weight multi-agent systems, complementary to output-level protocols for black-box agents.