Recommendation & Information Retrieval - Weekly Roundup

Inspired by Mixture-of-Experts (MoE) principles, Evolvable-Substrate HyperNEAT is partitioned into non-overlapping spatial segments, each assigned to a separately evolved specialist network, which forces evolution to discover features across the entire image.

MultiHuSE is introduced, a multi-modal dataset comprising 2,407 high-definition videos of 50 demographically diverse actors performing 1,463 text samples across four psychological humour styles (affiliative, aggressive, self-enhancing, and self-deprecating), as well as neutral content that provides empirical support for psychological theories linking humour and emotion.

AvaImg, a multi-stage optimization pipeline, is presented: it enforces body-inside-clothing constraint via signed winding numbers, is made viable by a three-level efficiency cascade (~10x runtime reduced, ~95% storage saved), and recovers fine surface detail using coarse-to-fine displacement optimization.

StepAudio 3 Realtime, an audio-language foundation model organized around a continuous listen-converse-think-act loop, achieves dialogue and reasoning performance comparable to dedicated reasoning models while speaking in real time, and resolves the tension between deep deliberation and latency via Think-While-Speaking.

This work shows that specialist optimization implicitly selects from this latent trajectory space, and establishes a new view of specialist training: when gold reasoning is absent, tuning choices directly control the latent supervision passed to downstream models.

StepAudio 3 Realtime, an audio-language foundation model organized around a continuous listen-converse-think-act loop, achieves dialogue and reasoning performance comparable to dedicated reasoning models while speaking in real time, and resolves the tension between deep deliberation and latency via Think-While-Speaking.

This work shows that specialist optimization implicitly selects from this latent trajectory space, and establishes a new view of specialist training: when gold reasoning is absent, tuning choices directly control the latent supervision passed to downstream models.

Continual Search is introduced, an iterative framework that nudges the judge, over successive turns, to keep searching for unresolved diagnostic evidence within multiple benchmark suites and model families, demonstrating that effective search supersedes raw model scale.

Latent Interface Training (LIT), a framework-agnostic two-stage strategy that first establishes a spatial-goal-conditioned action prior without images, then constrains visual conditioning through a pose-supervised latent interface, improves overall LIBERO-Plus success while preserving or improving average LIBERO success.

UFO is proposed, the first unified framework for omni-condition alignment simultaneous evaluation, and UFO-Bench is presented, a dedicated benchmark designed to holistically evaluate the performance of existing customization models under the diverse mutual interactions of textual and visual conditions.

Across reasoning, long-context understanding, and agentic tasks, SAS consistently outperforms trainable sparse attention baselines across attention budgets, with especially large gains under tight budgets, demonstrating more effective context ranking for downstream tasks.

This work introduces Minimal Intervention Reinforcement Learning (MInTRL), which expands the exploration frontier through sparse, local interventions in otherwise on-policy rollouts, and establishes minimal intervention as an effective paradigm for enhancing on-policy RL.

NGU, a simple adaptive sampling method that keeps generating samples for a problem until one is correct, is introduced, a simple adaptive sampling method that naturally uses fewer samples to filter out easy problems and allocates more compute to solving harder problems.

Dynin-Robotics achieves competitive performance on LIBERO and zero-shot LIBERO-Plus, together with a 78.4% average success rate across four manipulation conditions on a Franka Research 3 robot.

eConv-TasNet is proposed, an efficient variant of Conv-TasNet that improves both effectiveness and efficiency without relying on resource-intensive modules, and achieves competitive performance compared with state-of-the-art methods while requiring significantly fewer parameters and lower inference cost.

UniH3, a novel framework that Unifies Hierarchical Homogeneity and Heterogeneity and Heterogeneity for all-in-one medical image restoration, is proposed and extensive experiments on two large-scale benchmarks demonstrate that UniH3 achieves state-of-the-art performance on both all-in-one and single-task medical image restoration.

Results show that structured schema context and lightweight prompting can substantially reduce reliance on fine-tuning for scalable conversational access to KGs, though closing the remaining gap to fully fine-tuned approaches will likely require reducing dependence on curated question-query exemplars.

The transition from file storage to database management systems transformed stored data into managed resources into AI model management systems, and AI now faces an analogous transition from AI model storage to AI model management.

Modern VLMs already encode much of what is needed for specialized domains; the challenge is learning how to ask, suggests modern VLMs already encode much of what is needed for specialized domains; the challenge is learning how to ask.

The regulatory sandboxes can be viewed as pedagogical environments for AI: dynamic spaces where alignment develops as a formative process, progressively shaping autonomous behaviors through interaction and cooperation in scenarios of increasing complexity.

Experimental results demonstrate that DRG-MAPPO achieves a state-of-the-art win rate of 87%, suggesting that the framework effectively balances relational modeling, interpretability, and optimization stability for cooperative air combat.

Generative Late-Interaction Embeddings (GLIE): k<<N vectors per page learned from the normalized centroids to serve as both a lightweight index and a basis for regenerating the page's full embedding set, with the decoder as its main design surface.

Textual descriptions of reaction conditions are encoded by a fine-tuned language model trained jointly with Gaussian process surrogates, yielding task-adaptive representations within a multi-objective Bayesian optimisation loop.

TART is the first framework to generate guitar tablature with both fingering and expressive technique annotations directly from guitar audio, and is the first framework to generate guitar tablature with both fingering and expressive technique annotations directly from guitar audio.

This work develops Generative Marketing Mix Modeling (GMMM) to estimate the causal effects of Generative Engine Optimization and Generative Engine Marketing and investigates the empirical performance of the proposed method using simulated answers to product recommendation in English and Japanese.

SIRF (Spec-Internalized Risk Foundation Model), which internalizes a platform's complex policies, synthesized without additional human annotation via EntiGraph, MAGA rewriting and account-level chain-of-thought (CoT), into the weights via continued pretraining (CPT), so rules are applied at high precision under an ultra-low-latency, verdict-only deployment.

It is found that cache performance depends on transfer granularity, intermediate memory use, and when transfers enter the request schedule, not only on device bandwidth, not only on device bandwidth.

A certification layer is developed that wraps any severity model unmodified, with distribution-free guarantees using this structure, and attaches identical validity and certifies, on the vulnerable road users, a model-independent floor on set width that no base model beats.

This study examines how synthetic and real historical training data should be combined for low-resource OCR, and finds that joint and sequential training yield broadly similar archival accuracy under the tested practical pipelines.

Equal parameter count evaluations show that modulating the primitive's wavefront is an effective and lightweight enhancement for hologram representations, and frequency domain analysis illustrates that CVQPG has successfully preserved the mid-to-high frequency band of natural images.

Inspired by Mixture-of-Experts (MoE) principles, Evolvable-Substrate HyperNEAT is partitioned into non-overlapping spatial segments, each assigned to a separately evolved specialist network, which forces evolution to discover features across the entire image.

This work revisits RAG compression from a data-mining perspective by aggregating historical query--document--model interactions into reusable evidence views and proposes Reusable Evidence View Aggregation (REVA), a framework that mines the target generator's historical attention traces into a document-keyed, budget-agnostic score store.

Within the tested range, concentration sets neither the destination nor the pace of collapse; the pace follows whose text fills the pool; what emerges instead is an invariance.

This paper proposes a meta-learning framework that leverages a comprehensive set of meta-features capturing dataset complexity to predict classifier performance without exhaustive training, and achieves an average ranking prediction accuracy exceeding 86%, demonstrating its effectiveness in guiding model selection.

SolCloudLLM aligns sky-image patches with time-series patches and fuses their corresponding representations through bidirectional multimodal fusion, yielding a unified representation that is subsequently mapped into the embedding space of an LLM.

Ecdysis is proposed, an efficient and effective framework that distinguishes model-specific accommodation from harness-level repair and biases adaptation toward systematic harness deficiencies by identifying recurring cross-task failure patterns.

Techniques for improving speech recognition-based transcript creation in multiple languages from videos to better train these automated tools are presented.

AssayLoop is introduced, a sequential experimental design framework combining AssayFormer, a transformer-based amortized acquisition policy trained across historical screens to adapt from experimental feedback, with LLM-derived biological priors through an adaptive handoff to demonstrate the value of learning acquisition policies across historical experiments and combining them with broad biological priors for efficient adaptive hit discovery.

COBRA-Skills is introduced, an efficient framework that formulates skill optimization as budgeted sequential optimization over a dynamically evolving candidate space and remains robust to changes in the agent harness and performs effectively when the target model itself is used for skill generation and refinement.

It is argued that the graph provides a concrete setting for developing AI-driven metadata enrichment and entity resolution, and its coverage skew toward American, city-level data.

This work proposes a two-agent framework that first identifies the firms for which each alternative data channel is likely to be informative and then predicts revenue using firm- and channel-specific context and evaluates the framework across four commercial alternative data channels.

A model, InterIL, is proposed, which jointly generates the two modalities, background image and layout, in a single generative process and has no design-specific inductive bias, which allows it to better preserve the original characteristics of realistic designs.

This paper introduces Agent-Integrated Software (AIS) as a software pattern combining a conventional core, direct interaction, and a built-in agent, and Intent-Level Interaction Abstraction (IIA) as the task semantics through which users inspect and control delegated work.

Results show that failure-aware, evidence-constrained refinement can improve routing heuristics on difficult instances while preserving performance on cases they already solve well, and show that failure-aware, evidence-constrained refinement can improve routing heuristics on difficult instances while preserving performance on cases they already solve well.

This work proposes an innovative solution that combines a lightweight vision-language encoder with a compact multi-layer perceptron to predict combustion efficiency directly from low-cost thermal video footage to achieve high combustion efficiency in flare stacks.

Results are reported, showing an inverse relationship between benign and unsafe capability, strong evidence that baseline safety guardrails do not lead to downstream safety guarantees in the RAG case, and model-specific support for previous findings that even benign documents can lead to unsafe generation in retrieval-enabled systems.

The first large-scale audit of the default moderation system on Bluesky is conducted, revealing a human-AI collaborative system where labels for sexual and graphic content are applied automatically in seconds, while nuanced and high stakes labels require more human oversight, taking hours or days.

This work proposes two approaches of code-mixed generation using parallel sentences of three languages and fine-tune contextual transformer-based models MuRIL and XLM-RoBERTa best suited for Indian languages for language identification in code-mixed settings.

INDRA is an LLM-powered research partner enabling massive multi-archival investigations, a tool whose outputs are designed to be checked rather than trusted, and whose architecture makes the conditions of knowledge production visible and auditable.

This work benchmarked ten classifiers spanning linear, tree-ensemble, neural, glass-box, and tabular foundation classes for prevalent myocardial infarction in 442,067 respondents of the 2022 Behavioral Risk Factor Surveillance System across five feature tiers of decreasing leakage risk.

MultiHuSE is introduced, a multi-modal dataset comprising 2,407 high-definition videos of 50 demographically diverse actors performing 1,463 text samples across four psychological humour styles (affiliative, aggressive, self-enhancing, and self-deprecating), as well as neutral content that provides empirical support for psychological theories linking humour and emotion.

ProMediConv is introduced, a novel benchmarking framework that models mediation as a proactive, multi-stage, and party-aware dialogue process incorporating 11 mediation strategies and four party behavior pattern (BP) states and proposes MAD (Mean Attribute Difference), a fine-grained metric that captures BP shifts throughout the dialogue.

Financial NLP has a standard workflow: validate a sentiment tool against human labels, then trust it to extract market signal, but it is found that the relationship between construct and predictive validity depends on the sampling convention and score representation.

Distribution-aware Language Neuron selection is proposed, which leverages pairwise relationships between per-language activation distributions over the full activation range, including negative values, to quantify each neuron's language specificity by clustering languages using pairwise overlap coefficients between their activation distributions.

This work specifies terms.txt, a robots.txt-style file for per-path, per-purpose machine-access terms, plus an origin-enforced exchange using Web Bot Auth signatures, signed intent, delegation tokens, HTTP 402 negotiation, and signed receipts, and defines what the exchange can enforce, audit, and leave to contract.

An annual 1km maps of the probability of 2G, 3G and 4G coverage for 214 countries and territories for the years 1999 to 2030, which supports mapping the global digital divide, linking connectivity to household-survey outcomes, and humanitarian and infrastructure planning.

This paper reverses the question: how can digital transformation make established human institutions more accountable and theorizes this reversal as accountable digitality and specifies needs-aware information systems as the mediating mechanism.

This work derives the first model of WhatsApp Web's implementation of the Signal protocol and the most detailed model to date of Signal's original protocol, and reveals previously undocumented differences between the original libsignal library and WhatsApp's fork.

Atlas is the first to demonstrate verifiable graph-based search at scale, proving a query in under a second on the SIFT1M benchmark and in 2.0 seconds at 100 million vectors, while maintaining the recall of plaintext HNSW and revealing nothing about the index beyond the result.

This paper formalizes four types of Solana-bound cross-chain transaction modes and proposes a candidate-set selective decision-based tracing method called SolTracer, which outperforms state-of-the-art (SOTA) methods across three representative scenarios: closed-world association, open-world association, and cross-source-chain generalization.

This work systematically reviews FSL approaches for NIDS published from 2022 to 2026 with PRISMA 2020-like reporting to search ACM Digital Library, IEEE Xplore, and Scopus, and compares reported performance.

A descriptor of GEMM reduction order is introduced, and the first black-box reconstruction of a closed-source library's arithmetic for bit-level correctness is performed, including the first black-box reconstruction of a closed-source library's arithmetic for bit-level correctness.

An automated bug triage system built upon the pre-trained RoBERTa-base transformer architecture is proposed, which achieves a strong bug identification accuracy within just five training epochs and highlights the efficiency and high performance of fine-tuned transformer models for practical software engineering automation.

AvaImg, a multi-stage optimization pipeline, is presented: it enforces body-inside-clothing constraint via signed winding numbers, is made viable by a three-level efficiency cascade (~10x runtime reduced, ~95% storage saved), and recovers fine surface detail using coarse-to-fine displacement optimization.

A mechanistic analysis of the LW-DSC metric is provided that explains why recall-recovering post-processing carries low risk whereas component deletion does not, and thirteen negative results spanning loss engineering, alternative backbones, and inference-time settings are reported.

OststaDiff is presented, a decomposed diffusion-policy framework with a lightweight obstacle-aware visual encoder that extracts a structured target-obstacle-background representation, enabling the downstream alignment policy to generate end-effector trajectories toward a target-centered bottleneck pose while reasoning about surrounding obstacles.

This work proposes a Visual-SLAM system using a monocular camera, which is significantly more cost-effective and specifically tailored for agricultural applications, such as mapping tomato crops in a greenhouse.

SwarmNxt, an open-source software platform built on the open-source OmniNxt drone hardware, is presented, providing an open research infrastructure for physical swarm experimentation and validated through two real-world experiments.

In the R-VP setting, in which in every step, the vertices are arranged as a ring according to a random permutation, it is shown that treasure hunt takes expected $\Theta(n)$ steps against an oblivious adversary and $\Theta(n \log n)$ steps against an adaptive adversary.

LASP is introduced, a Language-Augmented Semantic Priors framework that leverages large language models to infer structured, solver-usable B-spline priors from procedural modeling histories, establishing a new paradigm of language-guided geometric optimization in modern CAD systems.

The results separate indiscriminate poisoning, which shows up in standard metrics, from targeted backdoor poisoning, which stays comparatively stealthy while embedding highly effective malicious behavior, and they support security-oriented evaluation beyond conventional clean-test metrics.

Sewer-Transformer-ML is developed, a hierarchical vision Transformer with multi-level feature fusion together with two lightweight architectures, Sewer-MobileNet-ML and Sewer-Mobile-TransNet, for resource-constrained inspection scenarios and provides a computational basis for automated sewer inspection, lightweight model design, and adaptation across civil infrastructure inspection platforms.

This work proposes UniRec, a Unified Cross-stage Recommendation Fusion model, and introduces a dual-axis preference alignment objective, and finds that unconstrained end-to-end fusion optimization can exploit imbalances in item attribute distributions, over-concentrating on high-reward regions at the cost of other objectives.

Findings support Brain-PACE as a complementary longitudinal imaging phenotype with sensitivity to relevant clinical and biological changes in early neurodegeneration and extend the LILAC framework by combining spatial attention with soft label distribution learning and a Cram\'er distance objective.

Omni-Modal Gait Recognition is formulated, which unifies single-modal, cross-modal, and multi-modal recognition within a shared identity space and remains competitive with task-specific experts in many shared settings and extends to higher-cardinality fusion unavailable to fixed-pair models.

The result is a practical way to separate two failure modes that are usually mixed together: a weak selector versus an information channel that cannot support the desired decision in the first place.

VP-CONTROL, a runtime-assurance design and deterministic benchmark for cost-aware commit gates, is presented, motivating explicit evidence lineage, cost-aware selection, and commit-time enforcement, while exposing the limits of approximate calibration and local-tool generalization.

ToxicRAG is presented, a one-document-per-target attack that expresses misinformation as a coherent knowledge-update narrative that matches or exceeds the strongest evaluated baseline in every combination of dataset--model combinations.

This work undertakes the first comprehensive investigation into how sampling methodologies affect the measurements and the conclusions, and proposes an adaptive probability-based sampling strategy that remains effective even when the prevalence of the target issue is unknown.

Negative Self-Distillation (NSD) is introduced, a new framework that optimizes LLMs by diverging from flawed reasoning rather than imitating privileged solutions, and consistently outperforms OPSD and other label-free, self-bootstrapping reinforcement learning (RL) baselines.

This work reifies the input graph by reifying the input graph: every fact becomes a node, connected to its subject, object, and relation type through a fixed vocabulary of six meta-relations, with relation types as anonymous shared nodes rather than model parameters.

GenQAS is introduced, a tensor network-guided RL framework that combines a fixed matrix product state warm-start with prioritized generative replay and can mitigate sample starvation in quantum architecture search and support more resource efficient circuit discovery.

This work introduces a model-aware schedule construction based on fiberwise optimal transport, and evaluates DDPMs and flow matching across prediction targets, training configurations, risk-estimation checkpoints, datasets, and architectures.

This work introduces AmazonSWE, a dataset for training and evaluating large-scale spatiotemporal graph imputation methods that integrates processed satellite altimetry measurements from a range of sources, including the recent wide-swath SWOT sensor.

The results show that differentially private perturbation can be integrated into EEG processing workflows, but the selected mechanism, privacy parameters, and sensitivity calibration strongly influence data utility.

This paper proposes a key notion: $\gamma^{*}\!$-concept shifts, and derive a general error bound unifying covariate and $\gamma^{*}\!$-concept shifts, which applies to broad loss functions, label spaces, and stochastic labeling and develops estimators for these shifts with concentration guarantees.

This work introduces CoRA-NAS (COarse Ranking + Anchor-residual), a two-stage framework combining a static ranking prior with low-cost learning-curve refinement that combines cross-space ranking robustness with low-cost architecture selection.

The problem of maximizing the weighted average of performance certificates in the presence of uncertainty about the true system state is shown to be equivalent to an optimization over nested prediction sets, connecting to the literature on conformal prediction and extending prior art on single-level risk-averse decision making.

The proposed Koan-Safe, which combines a prompt-only intent parser, a replaceable generator, and structural repair with default safety parameters, is proposed, which supports explicit trade protections and execution-based evaluation, while distinguishing declared safety from a general guarantee.

A directory-aware semantic data management system that tightly integrates semantic and structural access to support structural-context-efficient, evidence-gap-driven multi-round retrieval, and introduces an adaptive escalation strategy that answers from one-round experience-augmented retrieval when the evidence is sufficient, and invokes agentic multi-round retrieval only otherwise.

HerALD (High-fidelity Exemplar Retrieval with Adaptive Landmark Distillation with Adaptive Landmark Distillation), a gradient-free graph condensation framework that adapts the node scoring and feature selection in the condensation pipeline to the graph's measured heterophily, is proposed.