ROAR is a Retrieval Opportunity-Aware Refinement framework designed for zero-shot time series forecasting. It enhances predictions by weighting queries according to base-forecast difficulty and the improvement potential of retrieved historical candidates, then learning a gate to control correction strength. The method jointly calibrates the forecasting module and gate, anchoring the final prediction to an initial refined output, and achieves the lowest average MSE across seven benchmarks compared to other methods.
By Jinjin Chi, Lei Feng, Yiming Wang, Lulu Zhang, Yongcheng Jing, Chenyi Li, Ximing Li
The paper introduces RAG‑Stress, a diagnostic protocol that tests how retrieval‑augmented generation models handle misleading evidence. By fixing the question and reference answer, editing a single assertion to support an incorrect answer, and varying source‑priority policies and evidence positions, the study measures misleading rate (MR) and clean accuracy across fifteen systems on multiple QA datasets. Results show that prioritizing documents increases MR by 10.9–13.5 percentage points, with the highest MR when the misleading span appears at the end of the evidence.
By Shunyuan Zhou, Hao Chen, Tianyu Wang, Goose Lin, Zaiyuan Wang, Haiying Zhao
MAP4CS is a data pruning framework that selects a small, high‑quality core subset from large code corpora by combining syntactic structure, semantic diversity, and distributional representation, followed by rule‑based filtering. Experiments on two large datasets show that using only 5% of the training data, MAP4CS outperforms random sampling and matches or exceeds full‑corpus fine‑tuning, supporting the idea that less data can be more effective. The method also automatically removes duplicates and noise, producing a lexically diverse and information‑dense training set.
By Yuxuan Chen, Mingwei Liu, Guangsheng Ou, Zekai Zhang, Zike Li, Yanlin Wang, Pelin Zheng
Syn-Omni introduces a structured approach to omnimodal embeddings by separating universal semantics from modality-specific features through Orthogonal Modality-Expert LoRA (OME-LoRA). It further employs Progressive Synergy Routing (PSR) to allow modality experts to first learn modality-specific priors and then gradually collaborate across modalities. Across 81 tasks involving image, video, audio, and audiovisual data, Syn-Omni outperforms existing omnimodal baselines, highlighting the benefits of structured specialization and progressive cross-modal collaboration.
By Youngtaek Oh, Qiyu Wu, Hiromi Wakaki, Junmo Kim, Yuki Mitsufuji
READ‑Bench is a new benchmark for historical‑case retrieval in time‑series diagnosis, covering 12 multivariate datasets and defining relevance by shared fault or event type rather than signal shape. The study evaluates a wide range of retrieval methods—including classical distances, symbolic retrievers, self‑supervised and foundation‑model embeddings, and label‑aware or language‑model rerankers—under a unified protocol that varies supervision, pollution, and corpus size. Results show that pretrained embeddings do not outperform strong classical baselines for search alone, but a small amount of resolved‑case supervision via a Gaussian‑process reranker significantly boosts performance, especially when combined with a reciprocal‑rank fusion of a normal‑residual embedder and dynamic time warping.
whyItMatters":"The benchmark demonstrates that modest supervised reranking can outperform sophisticated representations, guiding future research toward efficient, data‑light retrieval strategies for time‑series diagnosis."
By Gerardo Pastrana, Haojun Li, Dhruv Mehta, Anoushka Vyas, Sina Khoshfetrat Pakazad, Henrik Ohlsson, John Paparrizos
Humanize is a multi‑agent workflow for agentic coding that uses explicit, mechanically enforced decisions—called judgement engineering—to separate planning, implementation, review, and learning. A human approves a plan contract, a builder agent implements it in rounds, and a reviewer agent from another vendor decides completion, with deterministic hooks and 72 mechanical gates ensuring that a defect survives only if both models miss it. The system has been deployed in over 68 versions in 108 days, earned 1,468 GitHub stars, and powered applications such as a 567‑file gem5 build‑system migration, Kernel Design Agents that excelled in MLSys 2026 contests, and Humanize Olympiad Agents that achieved top scores in multiple international competitions and high rankings on PutnamBench and Lean‑Eval.
whyItMatters":"Humanize demonstrates that a structured, multi‑agent approach with explicit mechanical gates can improve reliability and performance in automated code generation, as evidenced by its adoption, star count, and success in competitive benchmarks."
By Sihao Liu, Ligeng Zhu, Zijian Zhang, Dongyun Zou, Zhengyang Zhang, Changye Li, Song Bian, Song Han, Tony Nowatzki
The paper introduces MGRASRec, a multimodal graph retrieval-augmented framework that enhances sequential recommendation by incorporating collaborative filtering signals directly into a multimodal large language model (MLLM) prompt. It retrieves structured paths from a user-item interaction graph, extending them with multimodal similarity to broaden coverage and surface the most relevant history items for each candidate item. This approach eliminates the need for recurrent summarization, requiring only a single forward pass per candidate and achieving superior performance across three datasets, especially in ranking quality.
By Jason Marcell Setiadi, Xin Cao, Lina Yao
The paper introduces QGMem, a memory framework for conversational agents that organizes dialogue history into event-indexed atomic units and consolidates related units into dynamic memory traces. When a query is posed, QGMem retrieves complementary candidate memories, reranks them to form a compact working memory, and represents this working memory as a local graph to expose relational dependencies. Experiments on six benchmarks demonstrate consistent improvements in retrieval, multi‑hop evidence composition, conflict resolution, and ultra‑long dialogue reasoning while keeping context size and inference cost moderate.
By Yichen Liu, Chunfeng Yuan, Haowei Liu, Wenjuan Li, Zefeng Lin, Bing Li, Xu Chen, Weiming Hu
The paper introduces CogMem, a cognitive memory architecture for long‑term dialogue agents that uses a PEC$^2$F graph schema to separate claims, facts, and events. Dialogue turns are incrementally turned into provenance‑aware graph records, consolidated into higher‑level facts, and reconciled into temporally scoped claim views when conflicting updates arise. Retrieval is performed by a rule‑based controller that applies deterministic graph operators—anchoring, traversal, intersection, and evidence grounding—guided by LLM intent parsing, achieving strong performance on multi‑hop, temporal, and knowledge‑update tasks in LoCoMo and LongMemEval benchmarks.
By Zirui Liao, Zhengxian Wu, Zhuohong Chen, Yunyao Yu, Xiaoyu Liu, Yifan Xu, Haoqian Wang
The paper introduces HRIL, a method for learning multimodal representations that explicitly models higher‑order interactions among modalities using a cross‑moment tensor and Tucker decomposition. It adds a synergy‑aware regularizer to preserve higher‑order coupling capacity, aiming to capture synergistic signals that only emerge from joint configurations of multiple modalities. Experiments on controlled synergy tasks and real‑world benchmarks show consistent improvements over existing multimodal contrastive methods, especially on tasks dominated by synergistic interactions.
By Qun Dai, Liangjian Wen, Jiang Duan, Yong Dai, Dongkai Wang, Maolin Wang, Mingjie Wang, Jianzhuang Liu, He Yan, Zhao Kang
The paper reviews 25 years of automated verbal deception detection research, analyzing 289 reports and 3,653 models across 97 datasets. It finds a pooled accuracy of 74.4% with high heterogeneity, driven mainly by methodological quality rather than model complexity. Despite the use of embeddings and large language models, predictive performance has not improved, and only a small fraction of studies use verifiable ground truth or independent evaluation data.
Puffin is a probabilistic framework that disaggregates coarse spatial totals into high‑resolution estimates by leveraging satellite embeddings as covariates. Unlike deterministic methods, it learns a probability distribution for each fine‑scale subregion and trains with an aggregation‑aware likelihood, ensuring that predictions sum to the observed regional total. The approach provides calibrated uncertainty and does not require fine‑resolution labels, and it has been evaluated on German and US census, employment, and election data.
The paper identifies that visual instance retrieval failures are mainly caused by object-to-image (O2I) ratio mismatch rather than resolution loss, as shown on a benchmark of 3,021 Objaverse objects. Multi-scale architectures reduce resolution effects but still suffer from O2I mismatch, which is asymmetric across query and gallery sizes. The authors propose query-side scale augmentation and an OWLv2 crop reranker, achieving state‑of‑the‑art results on ILIAS 100M, and demonstrate that a LoRA fine‑tune can match these gains in a single forward pass.
BEACON-SP is an ontology‑grounded Graph Retrieval‑Augmented Generation framework designed to aid clinicians in suicide risk assessment by integrating diverse clinical, behavioral, social, and temporal data. It constructs patient knowledge graphs guided by a unified suicide prevention ontology that merges the Three‑Step Theory, the Integrated Motivational‑Volitional Model, and the Suicide Social Determinants of Health Ontology. In a benchmark of 1,500 queries across 15 clinical categories, BEACON‑SP outperformed a vector‑based RAG baseline in completeness, clinical relevance, and evidence grounding, with 76.4% of paired comparisons favoring the GraphRAG approach.
By Kemal Davaslioglu, Nathan Conger, Sastry Kompella, Yalin E. Sagduyu, Nathaniel D. Bastian
arXiv:2610.09285v1 Announce Type: new
Abstract: Many applications of physical AI run within finite or closed physical worlds with a limited set of physical laws governing object behavior. Examples in...
By Mayank Sengupta, Nirmit Desai, Eric Song, Kunal Sawarkar
arXiv:2604.20021v2 Announce Type: replace
Abstract: As Large Language Models (LLMs) become increasingly popular, caching responses so that they can be reused by users with semantically similar querie...
By Baran Atalar, Xutong Liu, Jinhang Zuo, Siwei Wang, Wei Chen, Carlee Joe-Wong
arXiv:2610.10124v1 Announce Type: cross
Abstract: Generative recommenders return a limited candidate set and may omit observed targets before reranking. A training strategy appends these missed targe...
By Xuesi Wang, Yangbin Shi, Xiaolin Zheng
The paper introduces SlideRuler, a method that uses internal controls within a pathology slide to estimate and correct scanner‑induced shifts in tissue representation. By learning a transfer map from paired rescans, SlideRuler can calibrate a frozen foundation model from a single scan, reducing embedding distance by 16.3‑38.5% across two encoders and five SCORPION scanners. A source‑anchored variant further reduces source‑feature displacement while preserving most alignment gains, demonstrating consistent calibration across imaging systems.
By Ming Ren Hou, Tianyi Huang
MeshCarve introduces a flow‑matching approach that generates artisan meshes entirely within compact latent spaces, separating vertex positions and edge connections to avoid joint encoding issues. It employs a hierarchical sparse transformer backbone with VertexVAE and EdgeVAE, which anchor on discrete vertices to drastically shorten token sequences and use spatial‑aware compression without sacrificing reconstruction quality. The method outperforms state‑of‑the‑art autoregressive and flow‑matching techniques on Objaverse and generalizes to Toys4K, representing one of the first fully compressed latent artisan mesh generators.
By Xiyu Wang, Ruocheng Wu, Yufei Wang, Zhihao Li, Lanqing Guo, Bihan Wen
PVSync is a unified model that estimates audio‑visual offset and scores phoneme‑level articulation for lip‑sync tasks. It uses window‑level contrastive learning for synchronisation and a phoneme‑level articulation objective that aligns audio and video embeddings of the same viseme class across clips. The model achieves higher correlation with human rankings and better performance on offset recovery and viseme matching benchmarks compared to existing methods.
By Kevin Stephen, Varun Menon, Timo Mertens, Nikita Drobyshev