SecLog-Bench introduces a benchmark for post-detection cybersecurity log reasoning that requires LLMs to infer attack progress from scaffolded multi-source evidence and produce structured, evidence-referenced reasoning chains. The authors construct 29 reproducible attack scenarios spanning CVE exploits and multi-hop attacks, aligning host audit logs, application logs, and network traffic into stage-labeled evidence windows. Evaluation of eight mainstream LLMs reveals modest attack-stage prediction (best accuracy 44.44%) and unreliable reasoning (best SRF1 54.87%), demonstrating that final-label accuracy alone misrepresents true model capability.

This paper introduces a ground-level dataset of 121,536 ground–reasons pairs extracted from 86,464 French civil cassation rulings spanning 2016–2025. Using a deterministic, rule-based pipeline, the authors pair each legal complaint with the Court’s response and classify the pair into established doctrinal families, producing a descriptive grammar of civil cassation. The resource enables quantitative analysis of which grounds succeed, which legal texts are cited, and how both evolve over time, alongside an interactive consultation tool.

Writing efficient kernels for Huawei Ascend NPUs is notoriously hard because AscendC code is scarce in LLM training data and operator deployment requires coordinating host-side tiling with device-side kernels. CANNBot-CAKE presents a full-stack agentic workflow that turns a natural-language request into a compiled, packaged, and benchmark-validated CANN custom operator. Its key ideas are a multi-DSL router that uses an item-response model to choose among four code-generation tracks, an evolution mode that iteratively refines kernels under a roofline model, and a cross-operator wiki (CAKE Book) that accumulates verified experience. On 53 CANN-Bench operators on Ascend 950PR, CAKE reports 100% standard-case and 98.28% hidden-case pass rates, with a mean score of 78.63/100.

LLM-native advertising introduces new auction-design challenges because an ad’s click-through rate depends on its context passage and earlier ads steal clicks from later ones under a first-click cascade. This paper studies truthful mechanism design when the LLM answer and its passage order are fixed before bidding, showing that a constant-factor welfare approximation is compatible with truthfulness even under this editorial-order restriction. The authors propose click thinning—randomly omitting earlier ads to equalize click services across positions—which turns welfare maximization into a tractable linear-fractional matching problem and yields an elegant optimal tradeoff between approximation, truthfulness, and computational efficiency.

The paper diagnoses a critical split in how LLMs handle Theory of Mind: explicitly recognizing an agent's belief versus applying that belief to predict behavior or evaluate actions. It argues that aggregate ToM benchmarks conflate these skills and hide severe applied deficits, then tests whether the gap reflects a capacity limit or merely latent competence that can be recovered via prompting, in-context learning, or lightweight parameter adaptation. The work is motivated by the deployment of LLMs in socially sensitive roles where surface-level mental-state recognition is insufficient for trustworthy interaction.

Early-stage structural design is an iterative, creative process that balances form, force, and material. This paper proposes a proof-of-concept workflow connecting a large language model agent to a COMPAS-based structural modeling API via a Model Context Protocol (MCP) server, enabling natural-language generation and editing of semantic building elements. By producing explicit graph-based artifacts rather than generic meshes, the work aims to reduce modeling friction and move toward human-AI co-design in structural engineering.

ColaBench tackles the problem of evaluating how personal agents use long-term interaction history to guide the acquisition and application of external knowledge. The core idea is a traceable benchmark built on an Evidence Graph $\mathcal{G}=(V,E)$ that links personal facts to their disclosures and to supporting documents, paired with MemKB, a retrieval method that connects state summaries and memory-document links. This matters because current benchmarks test memory recall and retrieval in isolation, leaving open whether agents can jointly recover historical conditions and apply newly retrieved rules to a specific user's situation.

Document-level LoRA adapters are pitched as modular knowledge units for retrieval and merging, yet standard open-book evaluation cannot tell whether an adapter has memorized a document or merely learned to read it well. This paper introduces a cloze probe that withholds the source text and finds that open-book scores are almost entirely generic reading behavior: a random adapter recovers 82% of a specialist's open-book advantage but only 2% of its cloze gain. The findings argue that adapter-bank evaluation needs a closed-book probe alongside open-book scores, and the authors' candid disclosure of four silent errors is a model of experimental hygiene.

Natural-language robot control requires mapping user requests to predefined tool subsets without inventing new capabilities. This paper frames tool routing as bounded multi-label selection over a fixed catalog and compares three ChromaDB retrieval policies against a cloud-hosted 120-billion-parameter LLM. The core finding is a clean quality-latency trade-off: clause-aware retrieval (CDBv3) runs locally in milliseconds but lags the LLM in routing accuracy, making it suitable for connectivity-constrained or privacy-sensitive deployments.

Neural networks trained independently on similar tasks often learn compatible semantic structure but express it through incompatible coordinate systems. This doctoral thesis proposes that semantic correspondence—identifying which elements refer to the same meaning across systems—is a fundamental primitive for revealing shared intrinsic geometry. It unifies fourteen methods spanning vision, language, reinforcement learning, and neuroscience under a single framework, distinguishing correspondence-parallel operators (independent semantic projections) from correspondence-coupled operators (direct cross-model maps). The work enables zero-shot transformation and model stitching without joint training, yet explicitly disclaims formal theoretical guarantees, positioning itself as a systematic empirical demonstration.

LAST-V2X addresses the problem of testing how delayed, missing, or corrupted V2X messages compromise safety-critical driving decisions. Its core idea is a framework that translates natural-language test descriptions into executable CARLA scenarios, then causally verifies whether any observed violations are actually due to degraded communication rather than ordinary traffic risk. This matters because existing adversarial methods perturb only the physical world, whereas V2X failures propagate from the information layer and must be isolated through paired counterfactual execution.

PRS tackles automatic heuristic design by making diversification an explicit generation-level operation rather than an emergent property of population management. The framework factorizes heuristic transformation into three semantic directions—expansion, compression, and steering—and rewrites perturbed ideas into executable code via LLMs within a breadth-first search with adaptive operator control. This matters because existing evolutionary frameworks mutate code without directly controlling the semantic trajectory of change, which often leads to premature convergence.

This paper addresses the cold-start forecasting problem for land-restoration sites, where physical models need site-specific hydraulic parameters and traditional data-driven models require months of per-site training data. The core idea is to evaluate TimesFM, a 500M-parameter time-series foundation model, in strict zero-shot mode on 15-minute in-situ telemetry from 27 soil-moisture and temperature sensors across three management regimes. The work matters because it systematically maps where zero-shot foundation models win and lose against classical and tree-based baselines, and quantifies the inference cost on commodity CPU hardware for operational deployment.

Autonomous driving vision–language–action (VLA) models promise explainable decisions by generating natural-language reasoning before predicting trajectories. This paper subjects that promise to a rigorous causal test via counterfactual interventions: the authors perturb model-generated reasoning text and photorealistically remove visual objects that the reasoning cites, measuring whether trajectories actually track these changes. Their findings across four state-of-the-art models indicate that the stated reasoning is weakly coupled to the resulting driving actions, raising serious concerns about the reliability of such explanations in safety-critical deployment.

When agents share a tool catalog and pool their usage experience, adding that usage to the retrieval index boosts tools with usage but can push unused tools out of the top results. This paper shows that folding usage examples into the index severely lowers held-out Hit@5 on two of three catalogs, and proposes Synapse, which scores documentation and usage as separate z-normalized fields before combining them with a learned weight. The finding matters because multi-agent systems risk silently failing on rarely-used tools unless the combination rule explicitly protects them.

Long-text image-text alignment underpins fine-grained scene understanding, yet existing models suffer from saliency collapse: they over-rely on visually prominent text while neglecting informative non-salient context. This paper proposes RoSA, a finetuning framework that decomposes image-text pairs into complementary salient and contextual views and enforces auxiliary contrastive objectives over both. By treating saliency as a multimodal training prior with no inference overhead, RoSA improves retrieval robustness when salient cues are weakened or removed.

Diffusion language models decode many tokens in parallel, making schema enforcement harder than in autoregressive models: two locally valid tokens can be jointly incompatible. DConstrain compiles schemas into context-parameterized transition programs that precompute local grammar steps while leaving the runtime stack and field history as unbound parameters, resolving them only during warm compilation. This factorization avoids the exponential blow-up that forces prior exact decoders to fix property order, yielding large gains in compilation coverage, decoding latency, and task accuracy.

Sparse attention must approximate the long tail of unseen KV-cache tokens, yet discarding it biases the output and random sampling misses heavy hitters at extreme sparsity. OpenAttention folds the tail into a few count-aware mean proxies discovered via query-geometry clustering, achieving 99% of dense accuracy while reading only 1% of the cache. The accompanying Triton kernel delivers a 10× speedup in the decode attention stage, making ultra-low-density attention practical for long-context inference.

RetiJev tackles the latency, cost, and privacy barriers of autoregressive LLMs in point-of-care ophthalmic decision support. The authors introduce RetiJev-Data, a 128k-question source-grounded corpus, and a family of 0.8B–9B compact models that score predefined candidate answers in a single pass instead of generating free-form text. This yields sub-200 ms inference per case on one GPU while reportedly surpassing a 27B LLM and a commercial decision model on guideline grading and clinical-action accuracy.

This paper tackles the challenge of translating natural-language optimization problems into correct, executable solver programs using frozen LLMs. It introduces OptiCycle, a lightweight inference harness that iterates code generation, execution, and repair in a persistent Python state, and OptiState, which adds an explicit five-element revisable formulation. Across nine benchmarks spanning 2,634 problems and 11 Qwen variants, execution-guided revision produces broad accuracy gains and outperforms heavier multi-agent workflows and post-trained specialist models without any parameter updates, though the extra formulation structure helps only some backbones.