Large language models (LLMs) often make reasoning errors when solving mathematical problems, and how to automatically detect and correct these errors has become an important research direction. However, existing approaches mainly focus on self-correction within the model, which falls short of the “teacher-style” correction required in educational settings, i.e., systematically guiding and revising a student’s problem-solving process. To address this gap, we propose SMRC (Student Mathematical Reasoning Correction), a novel method that aligns LLMs with student reasoning. Specifically, SMRC formulates student reasoning as a multi-step sequential decision problem and introduces Monte Carlo Tree Search (MCTS) to explore optimal correction paths. To reduce the cost of the annotating process-level rewards, we leverage breadth-first search (BFS) guided by LLMs and final-answer evaluation to generate reward signals, which are then distributed across intermediate reasoning steps via a back-propagation mechanism, enabling fine-grained process supervision. Additionally, we construct a benchmark for high school mathematics, MSEB (Multi-Solution Error Benchmark), consisting of 158 instances that include problem statements, student solutions, and correct reasoning steps. We further propose a dual evaluation protocol centered on solution accuracy and correct-step retention, offering a comprehensive measure of educational applicability. Experiments demonstrate that SMRC significantly outperforms existing methods on two public datasets (ProcessBench and MR-GSM8K) and our MSEB in terms of effectiveness and overall performance.

While LLM-based Multi-Agent Systems (MAS) demonstrate remarkable problem-solving capabilities, their interconnectivity acts as a conduit for the rapid spread of malicious injections. Addressing the limitations of static defenses, we present TopoSHIELD, a framework that reshapes the flow of malice via risk-aware topological evolution. Our approach utilizes a spatio-temporal graph neural network to monitor interaction dynamics, calculating node risk entropy (NRE) and edge attack conductivity (EAC) to pinpoint vulnerabilities. Guided by these metrics, TopoSHIELD executes precise structural interventions, pruning high-risk edges and isolating compromised communities to block attack diffusion. Empirically, TopoSHIELD reduces toxicity by 58% on GPT-4o while preserving high utility (>90% success rate), outperforming existing baselines in both suppression efficiency and scalability.

Multi-agent language systems are often deployed under the assumption that collaboration improves reasoning quality, but this assumption breaks down once token cost, latency, and collaboration-induced errors enter the picture. We study protocol selection under a cost-aware utility objective, where the system decides on each input whether to run a collaborative protocol or fall back to solo reasoning. We propose Causal Advantage Routing for Language agents (CARL), a contextual-bandit framework that estimates the conditional collaboration advantage of each protocol over solo reasoning. CARL combines confounded execution logs with budgeted randomized probes, forms one-vs-solo doubly robust scores and shrunken DR-derived pseudo-outcomes, and at deployment uses a one-sided predictive lower bound for a future realized protocol-solo contrast, otherwise abstaining to solo execution. Across math, code, and multi-hop question-answering benchmarks with an open-weight model, CARL improves cost-aware utility and substantially reduces harmful collaboration over fixed protocols, compute-matched solo strategies, MAS routers, cascade methods, and causal routing baselines, indicating that effective multi-agent systems should estimate when collaboration is causally worth its cost rather than invoking it by default.

Latent-based multi-agent systems replace parts of explicit inter-agent communication with hidden representations, offering a new direction for efficient and flexible agent collaboration. However, moving coordination into latent space may also move attacks beyond the reach of visible-text inspection. In this paper, we study whether latent states can carry attack-associated information that remains effective during clean executions. To examine this question, we introduce a latent attack framework that reactivates attack-induced effects through latent interventions without reusing adversarial text. Extensive experiments show that the resulting latent attacks can substantially degrade task performance in clean executions, especially when applied to inter-agent KV-cache handoffs rather than local hidden states. Further control analyses indicate that this degradation cannot be reduced to arbitrary perturbations or invalid generation. Overall, our findings suggest that latent-based collaboration does not remove attack risk; it shifts part of the risk into less observable execution states, calling for safeguards beyond visible-text inspection.

Practical video editing is not only pixel generation: an editor must turn a brief, a clip pool, music metadata, and hard constraints into an executable timeline. We study this decision layer as executable video-editing planning and introduce RefineCut, which, unlike workflow systems that wrap a prompted frontier model, trains a compact open-weight planner for it. The planner edits a typed timeline through structured patches covering clip selection, trimming, ordering, transitions, and duration and music alignment; a deterministic verifier applies each patch and checks it against an explicit constraint ledger. Because editing has no single ground-truth repair, we do not imitate teachers directly: RefineCut replays every multi-teacher branch through the verifier and keeps verifier-best repairs as supervision. A second stage, RefineCut-Evo, lets the student score its own repairs with the verifier and a task rubric and trains on high-margin preference pairs, so the final 8B planner runs in a closed verifier loop with no teacher calls at inference. On RefineCut-Bench (3,578 tasks, 7,971 captioned clips, 499 music tracks, explicit ledgers), verifier-replayed distillation lifts the planner from 0.620 to 0.858 on the protocol-specific Video-Editing Score and RefineCut-Evo reaches 0.924; the gain transfers to Llama-3.1-8B and GLM-4-9B, and in the same closed loop the 8B planner matches or exceeds its frontier teachers. Code and RefineCut-Bench are publicly released; see the Data Availability statement.