Agentic AI pipelines interleave LLM decisions, state changes, tool calls, and cross-agent delegation, so a single monitoring formalism cannot cover every boundary. This paper proposes AgenticRV, an architecture that separates execution-boundary adapters from monitors via canonical events, per-monitor projections and abstractions, and a policy layer that arbitrates normalized verdicts. Its core contribution is an interoperability contract with formal conditions for when observations suffice, when monitors can be replaced without changing decisions, and when blocking actually prevents effects.

CPS security analysts operate under need-to-know constraints that give them incomplete cross-domain views, risking flawed risk assessments. AWARE-RG addresses this by building a role-bounded abstraction lattice over Resiliency Graphs using MITRE ATT&CK tactics ordered by the ICS Cyber Kill Chain. It uses AI planning to validate attack-fault queries, repair incomplete analyst paths, and generate need-to-know explanations without exposing unauthorized predicates.

ReproBAIT evaluates whether sandboxed coding agents can reconstruct published biological AI tools from limited text descriptions when supplied with training data and a fixed compute budget. The authors task frontier LLMs with replicating three tools—a closed-source molecular-design model, ProteinMPNN, and RFdiffusion3—and score functional parity on held-out data rather than source-code similarity. Near-parity is reached on the two simpler tools for only a few dollars, while the most complex tool defeats all agents within the allotted budget, raising questions about memorisation and whether withholding code is enough to control access.

Theory-of-Mind reasoning requires connecting an agent's perception to their belief and then to their action, yet large language models often fail to compose these inferences even when each part is known. This paper proposes rule-based reinforcement learning: using Group Relative Policy Optimization to train models to emit concise if–then rules that bridge perception, belief, and action before answering. The authors show that this approach outperforms free-form chain-of-thought reasoning and supervised fine-tuning across four ToM benchmarks, while using roughly half the tokens, and that rules from larger models improve downstream GPT-4o-mini accuracy.

Reinforcement learning for language agents suffers from sparse trajectory-level rewards that do not directly supervise environment dynamics. DGS-WM addresses this by introducing Decision-Guided Selective World Modeling: instead of forcing the policy to predict entire environment observations as auxiliary targets, it selects only observation units that support the subsequent policy response and discounts already-mastered targets via confidence-adaptive weighting. This selective co-training yields consistent gains over full-observation counterparts across QA and web-shopping benchmarks.

StateCraft tackles the long-horizon inconsistency problem in video world models by explicitly decoupling a persistent, structured abstract state from the visual rendering pipeline. A finetuned vision-language model maintains and updates a graph-structured abstract state in response to actions, including off-screen changes, while a video diffusion model generates each frame conditioned on that state. This enables consistent revisits, direct state editing, and large empirical gains on a new GUI-world-model benchmark measuring memory and controllability.

The paper asks how much meaning language models can actually share through language alone. To measure this, the authors introduce two tools — an epsilon-estimation algorithm that finds near-optimal prompts and estimates how much performance is left on the table, and a reconstruction bottleneck that scores how well a receiver model recovers a sender model's concept from labeled examples alone. They then apply observational scaling laws across 1.2k qualitative concepts in eight domains, classifying each as already articulable ('reaches'), potentially articulable with stronger models ('rising'), or permanently tacit ('bounded').

Decentralised HVAC control in multi-zone buildings is difficult because each zone controller sees only local state while the zones share a single air loop. This paper assigns every zone an independent active-inference agent that learns its own transition model online, infers hidden coupling via a latent disturbance factor, and adjusts its energy weight in response to a live congestion signal drawn from the metered power of other floors, with no inter-agent messages. On five noisy test years the fleet achieves the lowest mean building peak ($88.3~\mathrm{kW}$) and the best coincidence factor ($0.929$) among nine controllers, while running the full fleet on a Raspberry Pi 5 with a p95 control latency of $442~\mathrm{ms}$.

Most rule-reasoning benchmarks present scenarios as a few flat facts, making it easy to spot relevant rules. RuleTrek instead distributes scenario evidence across heterogeneous files and decomposes agentic reasoning into Rule Identification (RI), Rule Deduction (RD), and End-to-End (E2E) evaluation. Experiments on 11 frontier models show that even the best agent achieves only 62.47% RI and 60.43% RD, while E2E caps at 55.29%, suggesting that distributed, multi-file scenarios pose severe challenges for current agents.

L3M asks how to run AI agents so that useful computation scales safely across days, machines, and model calls. It answers with a Git-backed, distributed runtime written in Lean 4 where capability tokens confine every tool call, trajectories are durable append-only logs, and agents self-extend by writing sealed programs whose types bound their authority. The goal is a harness where trust narrows rather than widens as agents grow more capable.

LiFi targets the task of generating metric LiDAR point clouds from cheap onboard multi-view camera images for autonomous driving. The core idea is a latent diffusion model guided by two decoupled branches: a geometry branch that aligns dense monocular depth estimates to the target LiDAR scanning pattern via a reverse sampling algorithm and uncertainty-modulated encoding, and a semantic branch that aggregates global cross-view context. If the approach holds up, it could enable large-scale data augmentation, cross-modal simulation, and sensor completion without requiring expensive LiDAR capture at deployment time.

Multi-turn language agents trained with trajectory-level rewards frequently suffer from misguided reinforcement: tokens inside undesirable behaviors can still receive positive advantages. The paper proposes Confidence-Guided Credit Correction (C3), a plug-in operator that selectively reverses positive advantages within detector-localized undesirable spans, scaling the correction by the behavior policy's confidence. Because C3 leaves rewards, returns, and advantage estimators untouched, it is compatible with both critic-based and critic-free optimizers, offering a lightweight and general form of process supervision.

Scientific taste varies across researchers, but learning individual preferences is hard when feedback is scarce and retrospective evidence only arrives after research unfolds. This paper proposes Privileged Hindsight Distillation (PHD), which trains a proposal-only student using later-emerging evidence to improve criterion assessment and population taste priors, then adapts the model to individuals with as few as 15 pairwise comparisons. The work matters because scaling research ideation requires filtering directions without collapsing diverse personal judgments into a single community consensus.

The paper tackles robust multi-agent driving policies that generalize beyond their training domain. Its core idea is UncArms, a self-play scheme where probing agents are rewarded for increasing neighbors' epistemic uncertainty ($\Delta \mathrm{Unc}$), automatically generating a hard-case curriculum without hand-crafted scenarios. This matters because prior conditioned diversity methods impose a "diversity tax" on driving performance, and this work is the first to show a conditioned policy surpassing a strong unconditional self-play baseline.

Large language models often satisfy formatting requirements by chance rather than genuine correction, and standard pass rates cannot tell the difference. ReFormBench addresses this by using each model's own unconstrained response as a behavioral baseline, then tracking whether feature-and-mode targets are corrected across instruction variants. It introduces TCPR for complete compliance and EMCR for stable correction, showing that the two metrics yield different rankings across ten models.

The paper tackles realistic gaze generation for a humanoid robot, separating the semantic choice of where to look from the physical motion of neck, eyes, and eyelids. Its Realistic Gaze Transformer (RGT) is a Transformer-VAE trained on retargeted human recordings and fine-tuned autoregressively to handle closed-loop rollout. Combined with an asynchronous vision-language model for target selection, the framework aims to make robot gaze more lifelike in real time. The authors benchmark kinematic fidelity but explicitly acknowledge that perceived naturalness and full pipeline accuracy are still open questions.

Laboratory experiments require adaptive, evidence-based decisions beyond rote protocol execution. LabOPBench introduces a 756-question multimodal benchmark in organic synthesis and materials science where each item pairs real laboratory evidence with procedural context and an open-ended judgment scored by expert-defined, question-specific rubrics. Evaluating eight frontier multimodal models reveals a sharp capability gap: even the best model reaches only 45.8%, below PhD-level human performance, with dominant failure modes in misreading evidence and contradicting it with proposed actions.

Multimorbid prescribing is a high-stakes coordination problem: patients with multiple chronic conditions receive fragmented, single-disease treatment plans that frequently trigger adverse drug events. PRISMATIC addresses this by treating prescription generation as a multi-agent synthesis task rather than monolithic text generation. The system deploys six specialized agents—ranging from information extraction to safety and rationality critics—on a shared blackboard, coordinated by an inner iterative-refinement loop (DFPC) and an outer long-term weight-adaptation loop (DSWA). The work matters because it reframes clinical AI safety as an explicit multi-objective coordination problem, moving beyond end-to-end LLM output toward modular, auditable reasoning.

Cake cutting asks how to divide a resource so that every agent values their own share at least as much as any other's, but when each agent must receive a single connected piece the computational complexity has been stubbornly open. This paper resolves the question by proving that computing an $\varepsilon$-envy-free contiguous allocation is PPAD-complete for normalized rational piecewise-constant additive valuations, even when densities take only two values and have at most six breakpoints. It also settles that finding contiguous EF$c$ allocations of indivisible goods on a path is PPAD-complete for any fixed $c\ge1$, using a novel pricing gadget that forces nearly equal prices regardless of how pieces are permuted among agents.

The paper tackles the token inefficiency of visible reasoning traces in Chinese LLMs, which are typically generated in modern vernacular Chinese. It proposes Wenyan Thinking, a method that rewrites intermediate reasoning into Classical Chinese (wenyanwen) to produce more compact traces while keeping final answers short and benchmark-compatible. The work matters because it treats linguistic register itself as a controllable compression axis for reasoning, offering a practical way to improve accuracy under tight generation budgets without architectural changes.