Multilingual language models often fail to recognize that two names written in different scripts refer to the same person. This paper introduces a controlled benchmark of 601 audited identity pairs with attested Devanagari and Latin forms, plus a 374-item Malayalam replication set, to isolate cross-script entity matching failures. The authors find that Qwen2.5-7B's same-person accuracy collapses from 35.1% in Latin-Latin to 8.5% in Devanagari-Devanagari under Marathi prompts, then use probes, calibration, and activation patching to diagnose whether the model retains latent label information and where it can be causally intervened upon.

RecallGS tackles online open-vocabulary semantic 3D Gaussian Splatting from streaming images. Rather than learning a heavy 3D U-Net to inject geometric context into 2D CLIP features, it treats the agent's observation history as a non-parametric prior: it retrieves relevant past frames with CLIP, verifies their geometric support via alpha-compositing weights, and fuses the verified historical semantics into the current Gaussians. The core claim is that this training-free recall-ground-fuse pipeline recovers geometry-aware semantic context without the inference overhead of an additional learned 3D branch.

This paper addresses the NP-hard Flood-It minimization and decision problems on colored graphs. The authors develop a tailored A* search using novel game-state equivalences, lower-bound heuristics, and pruning rules, and compare it against SAT, ILP, and CP formulations. The study matters because it provides the first systematic empirical comparison of exact solvers for Flood-It, showing that domain-informed search dominates on most modest instances while SAT scales better on the hardest cases.

Multi-agent LLM pipelines are widely assumed to outperform single-call setups, yet it is unclear whether gains come from typed inter-agent contracts or merely from decomposition into specialized prompts. This paper conducts an 18-condition controlled experiment on requirements classification and MoSCoW prioritization using three small language models on the PROMISE NFR+ dataset. The core finding is that most accuracy gains are associated with decomposition and task-specific prompts rather than with Pydantic-validated typed states, though the typed contract shows other quality-assurance properties such as earlier fault containment.

LLM email agents increasingly handle sensitive inboxes, yet conventional phishing benchmarks only test whether models classify emails as malicious, not whether they actually obey harmful requests inside them. This paper introduces AgentPhishing, a benchmark that scores both detection failure and compliance with malicious requests, and PhishOpt, an adaptive optimizer that evolves target-specific email-generation skills using agent feedback under a realism constraint. The core finding is that agents which safely handle static, pre-existing phishing emails can be driven to near-zero detection and high compliance when attacks are adapted to their behavior, exposing a major gap in current safety evaluation.

Meta-Diff reframes few-shot diffusion adaptation as reusable transport in a low-dimensional task space, avoiding per-target backbone retraining. A shared encoder maps source and target distributions to coordinates $z$, and a learned affine map $\widetilde{z}_T = A z_S + b_c$ predicts the target coordinate from a data-rich source without using any target images at deployment. This is especially consequential in domains like healthcare, where target data are scarce and collecting them is costly or privacy-sensitive.

This paper asks why modern AI hits ceilings despite scale, data, and compute, and answers that open-ended intelligence requires co-recursive semantic self-extension: a continuous cycling between object-level inference and meta-level transformation of both formal languages and goals. Drawing on Kant, Gödel, and Hegel, it argues that fixed architectures—from LLMs to Gödel machines—are inherently bounded, and that only collectives with persistent external memory can escape determinism. It is an ambitious attempt to reframe AGI as a logical process of indefinite semantic reconstruction.

Selective combinatorial optimization problems (SCOPs) such as the Orienteering Problem and Knapsack Problem require jointly deciding which elements to serve and how to route or pack them, causing sampled solutions to vary widely in objective value. The paper proposes Reward-Weighted Preference Optimization (RWPO), a training objective that scales each pairwise preference loss by the normalized reward gap between trajectories, preserving cardinal solution-quality information that standard preference optimization discards. This matters because binary preference signals struggle when subset selection creates sparse, high-variance reward landscapes, and the authors show that retaining reward-gap information yields more stable training and stronger generalization to large instances.

QICOM is an open-source desktop application that unites quantitative medical image comparison, paired radiomics, and blinded observer assessment into a single reproducible workflow. It tackles the fragmentation problem that forces researchers to juggle separate tools and scripts, losing the link between images, settings, and results. By saving native projects, configurations, and blinded response mappings, it enables any comparison to be reopened and recalculated, which is essential for trustworthy evaluation of image-processing methods and acquisition protocols.

Trion-1 tackles the fragmentation of protein modeling across specialized systems by unifying protein sequence, 3D structure, and natural language in a single 4B diffusion language model. Its core idea is to represent all three modalities in a shared discrete token space and model them with a non-causal diffusion backbone, enabling global residue interactions for structure prediction while retaining LLM-style language capabilities. The work matters because it attempts to turn six core protein capabilities—folding, inverse folding, understanding, and design—into composable generative primitives within one model.

CoWriter tackles passive human supervision in LLM writing tools by adapting cognitive-science co-authoring methodologies into a proactive agentic workflow. It deploys three specialized agents—Leader, Monitor, and Reviewer—to guide users through brainstorming, outlining, drafting, and revision while sustaining engagement and narrative control. A within-subjects study with 14 graduate-student participants shows authors feel substantially greater ownership and produce texts rated higher in human-touch qualities.

High-speed FMCG manufacturing loses billions to unplanned downtime because maintenance software often relies on associational predictions that can misrank corrective actions when confounders or mediators are present. This dissertation proposes PriMa-Causa, a framework that inserts a Causal Reasoning Layer between fault prediction and action recommendation, using a graph-conditioned tabular foundation model—pre-trained once on synthetic structural causal models—to answer interventional queries $P(Y \mid do(T{=}t), X{=}x)$ in a single forward pass. If the empirical chapters deliver on the design, the work could materially improve maintenance efficiency by targeting what actions do rather than what they merely predict.

Reward models score responses to steer alignment and filtering, but pairwise benchmarks compare only answers to the same prompt, leaving prompt-level score shifts completely invisible. This paper shows that eighteen released scorers exhibit far larger between-prompt variance than human ratings, proposes a three-part human-referenced audit to isolate the source, and warns that blindly subtracting prompt means can actually degrade global response selection.

Access-control systems increasingly combine mined rules with learned classifiers, relying on a small trusted shield and a region map to route decisions. This paper audits the two hand-built artifacts that sit between verified code and measured ML but are themselves unchecked: the hard-deny set read from policy prose and the region map produced by the mining pipeline. Through exhaustive soundness checks on public ABAC policies, blind LLM elicitation, and an exact decomposition of hybrid routing risk, it shows that prose-derived deny sets are frequently unsound, standard miners emit the wrong kind of rule set for hybrid routing, and the routing benefit cannot be read from a single loss metric. The results matter because these silent failures propagate the authority of a trusted shield into wrong denials and degenerate routing.

Text-to-music alignment is usually tested with a single global similarity score that hides which specific musical attributes are missing or misaligned. This paper proposes MQAScore, a fine-grained evaluator that uses a large audio language model to verify each atomic concept—across genre, instrument, mood\/theme, vocal, and usage dimensions—via binary question answering. The work matters because it moves beyond opaque global scores toward interpretable, per-dimension diagnostics that can directly guide improvements in text-to-music generation systems.

Automated survey generation has relied on flat document retrieval, which misses the evolutionary structure of research fields. SurveyGraph tackles this by constructing a Methods-Entity Knowledge Graph (MEKG) that encodes typed method-level relations—such as extends, improves, and applies—across papers. The graph then serves as a unified backbone for core-paper discovery, graph-aware retrieval, hybrid outline generation, and evolutionary narrative synthesis, aiming to make survey generation more auditable and structure-aware.

Scene retrieval requires compact image representations that preserve geometric invariance without sacrificing discriminative power. This paper proposes aggregating multi-scale, second-order CNN statistics using a Fisher Kernel framework, yielding a global descriptor for scalable search. The core idea is to replace explicit visual vocabularies with Gaussian mixture modeling and to pool Fisher-encoded features across image resolutions. The authors evaluate this on standard INRIA Holidays and UKBench benchmarks, claiming improved mAP over classical aggregation baselines.

T2Quant addresses the challenge of quantizing Diffusion Language Models (DLMs), whose iterative denoising process makes activation precision sensitivity vary across both tasks and timesteps. The core idea is a label-free proxy—combining local KL divergence with downstream influence—to predict where extra precision helps most, then allocating mixed activation bit-widths across denoising steps via exact dynamic programming under a fixed average budget. This yields consistent accuracy gains of up to 6.7 percentage points over fixed-precision quantized baselines on three DLMs and six benchmarks.

Long-horizon coding agents often notice failures yet keep following flawed plans. RIPPLE tackles this "Critique-to-Action Handoff Gap" by internalizing recovery supervision into a single self-correcting policy. It decomposes recovery into reflection triggering, diagnosis, and corrective action, using a prefix-only teacher and stage-specific RL credit that gates trajectory advantages with local validity signals. The result is a mean improvement of about 4.8 points across four software-engineering benchmarks without an online critic at deployment.

StateTrack-Bench studies whether LLMs can keep obeying a persistent rule across multi-turn conversations. The authors define State Integrity Collapse (SIC): a model remembers a constraint yet fails to enforce it in later answers, a failure mode they distinguish from ordinary forgetting. They evaluate seven pinned model snapshots across five constraint categories and five dialogue depths, showing that compliance decay is concentrated in symbolic rules and in one notably brittle model, and they argue convincingly that single-turn instruction-following metrics are insufficient for deployed assistants.