LabCompiler targets the translation of imperfect natural-language laboratory protocols into verifiable, executable human-machine workflows. The framework introduces a staged "diagnose-represent-execute" architecture: a protocol firewall repairs defects before compilation, a two-level device-agnostic graph separates experimental intent from hardware details, and a capability-grounded router assigns actions to automated devices or human operators. Reliable protocol execution across heterogeneous, partially automated labs is essential for closing the loop in AI-driven science.

This paper tackles the problem that LLM agents often return replies which a human requester cannot interpret because they rely on private working context unseen by the user. The authors define reply usability as the ability of a requester to understand and act on a reply from only what they have seen ($R$), and measure it with an LLM-based reader model that flags information gaps. Across eight current models, 54% of 480 replies fail, with even the best model failing 30% of the time. They show that appending explicit requirements or rewriting with a secondary model substantially lowers failures, making usability an actionable target for agent design.

Typed decision models return probabilities over fixed answer choices instead of generating free text, making them a natural fit for tabular prediction tasks. This paper evaluates four such models against three large language models on row classification, fact verification, and anomaly detection, subjecting every request to 21 content-preserving rewrites. The authors find that decision models match LLM accuracy at far lower cost and are far more robust to how questions are phrased, yet no model escapes fragility when the table layout is altered.

Simulated students let developers test tutoring systems before real learners are available, but LLM role-play suffers from competence leakage because the model substitutes its own knowledge for the learner's. HACHIMI-II counters this by recasting simulation as third-person prediction: it builds a student model, infers a fine-grained learner state for each step, and compiles that state into a deterministic behavior harness that constrains generation. On five datasets, this cuts the ability-distribution gap by 2.4–8.4× and more than doubles error-structure fidelity over the strongest baseline.

This paper tackles non-stationarity in long-term time series forecasting by asking how positional encoding shapes temporal dependencies in Transformer attention. The core idea is to reinterpret self-attention as a Bayesian posterior in which positional encoding acts as latent temporal evidence, and to replace static positional biases with an input-conditioned mixture-of-experts router that adaptively combines local and periodic kernels. The results matter because they offer a principled, lightweight, plug-and-play module that consistently improves forecasting across four diverse Transformer backbones.

Existing retrieval-augmented MT applies retrieval uniformly, yet LLM parametric knowledge suffices for many inputs and external evidence is only conditionally useful. This paper proposes SRAT, an agentic RL framework that treats reasoning, retrieval, and translation as learnable actions optimized via GRPO for final translation quality. It learns when to retrieve, what to query, and how to integrate evidence, supported by two retriever-alignment strategies and evaluated across idiom, knowledge-intensive, and document-level settings. The work is important because it challenges the default assumption that retrieval is always beneficial and shows that adaptive, quality-aligned evidence acquisition consistently outperforms fixed pipelines.

This paper challenges the dominant triplet contrastive learning pipeline for 3D shape understanding, which aligns 3D point clouds with rendered images and text. The authors argue that rendered images are unreliable due to domain shift, self-occlusion, and rendering artifacts, and propose learning from language supervision only. Their core idea, HyperCL, embeds 3D shapes and text into hyperbolic space via the Lorentz model, using a geodesic-based contrastive loss and a batch-wise entailment loss that enforces the partial-order relation $\text{caption}\,\text{entails}\,\text{3D}\,\text{shape}$. They also introduce c-OLVIS, a manually cleaned subset of Objaverse-LVIS, for more rigorous evaluation. The work matters because it questions a multi-modal pre-training assumption and reports state-of-the-art zero-shot classification and retrieval under a smaller parameter budget.

This paper tackles questions about an agent's history that require aggregating multiple events, a task that pure retrieval cannot solve. It proposes Trajectory Memory (TM) for deferred, source-preserving evidence selection, and TallyMem for executable event aggregation via a typed query language over a reusable ledger. The core insight is that aggregation load—the number of events an answer must combine—predicts failure better than raw history length, with strong controlled evidence (+11.0 to +13.8 points) supporting the mechanism, though full-benchmark gains are more modest.

Vision-language-action models enable instruction-conditioned robot manipulation, but they typically cannot improve from experience after deployment. This paper proposes VLRLA, a framework that equips frozen VLA models with in-context reinforcement learning by conditioning action generation on a recurrent memory of past observations, actions, rewards, and termination signals. Using algorithm distillation on constructed learning histories, VLRLA aims to gradually improve task success during deployment without updating network parameters.

Coding agents consume substantial tokens as they repeatedly decide where to look, what to try, and when to stop during automated repair. This paper asks which of these decisions can be offloaded to a cheap probabilistic judge that answers fixed questions without generating explanations. The authors introduce JevCoder, a workflow built around the proprietary Jev judge to recommend initial code snippets, guide action types during repair, and trigger submission. On 400 SWE-bench tasks across three repair models, the system cuts repair-model token use by $21.6\%$–$50.6\%$ while raising observed repair rates.

This paper tackles the problem that agentic scientific workflows can finish without crashing yet still emit scientifically invalid results due to silent faults, stale inputs, or contaminated shared ancestors. The core idea is a directed acyclic graph harness that treats trust as a release decision: it enforces mandatory checks, contains failures to affected branches, and uses a fixed risk model plus optional validators to decide whether a deliverable may be released. Language-model agents operate inside this layer, choosing corrective actions and reviewing releases, but cannot override it. On two synthetic workflows with injected faults, the authors decompose which harness component supplies which property, finding that structure, not the LLM, drives reliability.

MemCode targets the long-term memory bottleneck in LLM agents by arguing that standard continuous vector retrieval misses logically related but lexically distant facts. It proposes retrieval-oriented atomic memory units (AMUs) and indexes them with a learned discrete codebook via isomorphic semantic quantization (ISQ), enabling dual-path retrieval that combines dense vectors with associative codebook topology. On LongMemEval-S and LoCoMo, it reports the best overall accuracy and a construction runtime roughly $3\sim21\times$ faster than strong baselines.

Data augmentation for document-level NLP risks breaking inter-sentential coherence and invalidating sentence-aligned labels. This paper proposes an LLM-driven discourse parsing framework that preserves sentence order, count, and rhetorical relations $R_{i,j}$ while replacing key phrases $\mathbf{K}_i$ to generate augmented documents $\mathbf{D}'$. By keeping the $n$-sentence structure fixed, labels such as summary indices or QA spans can be transferred without expensive LLM relabeling.

Language models frequently derail on multi-step reasoning because a single early error propagates through the rest of the generation. REWIND attacks this by treating token-level entropy as a diagnostic: it traces backward from high-entropy peaks to the sharpest local gradient jumps, uses those positions as cut points for suffix regeneration, and filters proposals with a likelihood-based acceptance rule. The result is a targeted, test-time repair loop that requires no fine-tuning or external verifier and that the authors show improves accuracy across multiple model families and reasoning benchmarks.

Existing agent benchmarks generally assume cooperative, emotionally stable users, overlooking a major source of real-world failure. This paper introduces τ-Emotion-Bench, a ten-domain benchmark that evaluates tool-calling agents under controlled emotion-conditioned user interactions, together with Tracer, a pipeline that collects training trajectories through an interaction-aware harness where dissatisfied users can terminate inefficient exchanges. It matters because it reveals large reliability gaps in frontier models and shows that small models can be trained to substantially improve interaction consistency with far less data than prior methods.

PFedMA tackles reliable multi-round collaboration in Federated Multi-Agent Systems by jointly adapting agent selection and heterogeneous privacy rules as reasoning unfolds. Its core idea is a closed-loop orchestration that feeds response reliability and capability signals back into an RL-trained router, while a bidirectional local-global mechanism co-evolves privacy rules without overriding personalized constraints. This matters because existing approaches either select agents without historical accountability or enforce static privacy rules that cannot adapt to changing information needs across rounds, leading to error propagation and rigid privacy-utility trade-offs.

TEX-MEX attacks the central challenge in time-series contrastive learning: how to define similarity when semantic-preserving augmentations are scarce or unreliable. It unifies structural and temporal similarity by letting the encoder grade windows by cosine similarity, then pulling an anchor toward the time-shifted versions of similar batch windows and pushing it away from those of dissimilar ones. The result is a graded, efficient objective that learns what typically accompanies a physiological pattern in time, and it powers a 560K-parameter PPG foundation model that beats architectures roughly 50× larger.

Standard expected-reward RL suffers from mode collapse, concentrating probability on a few outcomes even when tasks require many distinct, high-quality solutions. MIPS tackles this by estimating marginal outcome probabilities through a learned trajectory-conditional model, avoiding frequency-based estimation that breaks down when the outcome space is much larger than the rollout group. Using the identity $P_\theta(o) = P_\theta(\tau) / P_\theta(\tau|o)$, it scales rewards by inverse estimated probabilities within a standard GRPO update. On enumerable Hypergrids, molecular synthesis, and phylogenetic inference, MIPS maintains reward-aligned diversity where group-count IPS collapses.

Advertising delivery still relies heavily on manual coordination across budgeting, bidding, and diagnosis, limiting scale and performance. AdCoEvo automates this pipeline by assigning Planner, Optimizer, and Inspector roles to an 8B-parameter language model and updating their LoRA adapters in rotation using settled business feedback, so the agents mutually adapt during continuous simulated delivery. The authors report gains of roughly 14% in advertiser value and 7% lower cost per acquisition over rule-based baselines in the AuctionNet simulator.

Wrist-worn IMUs power everything from gesture recognition to clinical motor assessment, yet most prior benchmarks evaluate each task in isolation, leaving unclear whether a single pretrained representation can serve all of them. This paper introduces IMU-Bench, a unified evaluation of ten tasks spanning kinematic estimation, semantic retrieval, activity recognition and Parkinson's assessment, and proposes InrtlWrist, a masked autoencoder pretrained on recorded and simulated wrist motion. The core finding is that pretraining yields broadly useful features—leading on nine of ten frozen-probe tasks—but that supervision from one downstream task rarely helps unrelated ones, distinguishing universal representation quality from task compatibility.