On-policy state training is a method for training chain embedding retrievers for multi-hop RAG on the states the encoder itself reaches, including those built on its own wrong retrievals. Each state is labeled with the earliest missing gold passage, other missing gold passages are masked, and updates are contrastive and mixed with teacher-forced states. It retrieves complete evidence more often than teacher-forced training and graph-based systems, and transfers to harder long-chain HotpotQA and 2WikiMultiHopQA variants the authors built.
RAGon-policy state trainingcontrastive learning+1
HotpotQA2WikiMultiHopQAarXiv
research, llm-apps, trainingยท Added 31 Oct 2026, published 29 Oct 2026
PrivacyEvalClaw is a tool-augmented deliberative agent that audits privacy leakage in LLM jailbreak responses. The paper is by Krishna Kanth Nakka and co-authors, and it was accepted to AACL-IJCNLP 2026 (arXiv 2610.36711, cs.CR). The tweet gives only the title, authors and venue, so the method and results are not described.
LLM
PrivacyEvalClaw
AI Securityยท Added 30 Oct 2026, published 29 Oct 2026
PrivacyEvalClaw is a tool-augmented deliberative agent for auditing privacy leakage in LLM responses produced under jailbreak prompts. By Krishna Kanth Nakka and five co-authors, the paper is on arXiv (cs.CR) and accepted to AACL-IJCNLP 2026. The fetched post gives only the title, authors and venue, so method details and results are not available here.
TCC-RAG is a retrieval-augmented generation method for temporal knowledge graph question answering (TKGQA). It turns the temporal knowledge graph into a multi-view causal cue graph, builds a query-specific temporal causal chain to guide retrieval, and applies a causal evidence filter to the results. The authors report gains of 10.72% and 8.40% on two TKGQA benchmarks and 62.97% on multi-step temporal reasoning.
RAGLLM
arXiv
research, llm-appsยท Added 30 Oct 2026, published 28 Oct 2026
Tessera is a training-free evidence delivery layer for web retrieval-augmented generation. It rebuilds retrieved pages' HTML into fine-grained evidence units, each completed with page title, section path and table headers, then packs the relevant units into the reader context at the same token budget. Tracing evidence over full English Wikipedia shows chunking loses table and infobox content. Tessera adds 5 to 6 accuracy points on three QA benchmarks and carries over to agentic search.
RAGHTML
TesseraWikipedia
llm-apps, researchยท Added 28 Oct 2026, published 27 Oct 2026
AK_QMUL's system for the ArGuard-2026 shared tasks fine-tunes JAIS-6.7B with 4-bit QLoRA and a linear head on the last-token representation. For binary safe/unsafe detection (B1) it uses a four-way head over dialect register and AraSafe MSA augmentation, reaching macro-F1 0.984. For seven-way harm domain (B2) it averages five seeds, reaching 0.790. Both ranked first. Bully is the weakest class.
JAIS-6.7BQLoRALLM
JAISArGuardAraSafe+2
research, ai-safety, trainingยท Added 28 Oct 2026, published 26 Oct 2026
HerHealthGPT is a conversational AI system for women's reproductive health that combines retrieval-augmented generation, context-aware clarification and source validation in a web interface. Evidence retrieval raises concern-category accuracy from 54.1% to 59.3% over fine-tuning alone on 540 messages from 90 cases. The clarification router reaches 85.6% recall and 86.2% specificity. In a 30-adult study, 83.3% preferred the retrieval-enabled version. The authors say clinical validation is still needed.
RAGLLM
HerHealthGPTarXiv
research, llm-appsยท Added 27 Oct 2026, published 24 Oct 2026
TriageRAG is a failure-aware self-training method for adapting retrieval-augmented LLMs to specialized domains using only an unlabeled corpus. It probes the student on self-generated questions with and without retrieved context, sorts them into known, solved, context-failure and retrieval-failure, and routes each to a matching training signal. With Qwen3-4B on 11 datasets it beats budget-matched SimRAG by 1.2-6.4 points and recovers closed-book accuracy where retrieval hurts.
RAGLLMQwen3
TriageRAGSimRAGCS-Bench+1
research, llm-apps, trainingยท Added 27 Oct 2026, published 24 Oct 2026
4DMemory is a benchmark suite for long-horizon spatiotemporal memory: the LongVideo dataset (about 900 hours of video, over 5,000 manually reviewed questions) and 4DMemoryBench. It also presents the 4DMemory Harness, combining four skills, 22+ tool models and a model-specific RAG system. Across 13 models, the harness improved every one by over 50%, and the same models scored over 10% lower than on a previous benchmark.
RAG
4DMemory4DMemoryBenchLongVideo+2
evals, multimodal, agentsยท Added 26 Oct 2026, published 23 Oct 2026
This paper describes an evidence-aware retrieval evaluation protocol for a clinical RAG system over breast lesion management literature. Relevance is defined by evidence spans mapped to all containing chunks. A hybrid retriever (dense, BM25, cross-encoder reranking) with 384-token chunks and 20% overlap reached 0.94 Recall@3 on 18 queries. Expanding ground truth from 25 to 37 spans did not change rankings. The authors note the small set needs independent validation.
RAGBM25LLM
DeepSeekDeepEvalarXiv
llm-apps, evals, researchยท Added 24 Oct 2026, published 22 Oct 2026
Every Part Has Its Role studies concept augmentation in LLM post-training, where a target word is replaced by related alternatives in training sentences. It extends prior work from nouns to verbs and adjectives. Concept augmentation improves perplexity most consistently on news regardless of part of speech, while verbs give the most reliable downstream gains. CKA and linear probing show the largest gains do not match the largest representation changes.
LLM
arXiv
research, trainingยท Added 23 Oct 2026, published 21 Oct 2026
FunCAD-Harness is a framework in which LLM agents design CAD mechanisms and revise them using physical simulation feedback. It turns abstract functional requirements into candidate-specific physical tests. The paper also introduces FunCADBench, a benchmark of 46 mechanism families with interdependent, multi-step operations. Experiments on frontier LLM agents show it exposes mechanical failures that visual inspection misses and raises functional success over standard simulation-augmented baselines.
LLM
FunCAD-HarnessFunCADBencharXiv
research, evals, agentsยท Added 21 Oct 2026, published 19 Oct 2026
EpiLens is a lightweight, open-source retrieval-augmented generation package that applies a fixed retrieval template and output schema to every paper in a corpus and links outputs to source passages. On a corpus of 2,297 epidemiology papers, its classifier matches or exceeds supervised and zero-shot baselines on four tasks without task-specific training data, scored against 214 manual labels. It also compares model-based study shares across parameters and pathogens.
RAGLLM
EpiLensSARS-CoV-2mpox+1
llm-apps, researchยท Added 21 Oct 2026, published 19 Oct 2026
E-Layout is an ML-driven framework for post-placement logic restructuring in IC physical design. It builds equivalent logic alternatives with e-graph equality saturation, links logic choice to physical realization through a graph-based RAG model, and refines the choices with reinforcement learning on design-level timing feedback. On 9 designs it gives about 6% TNS and 2% WNS improvement and 1% less area than a commercial physical design flow.
RAG
E-LayoutarXiv
researchยท Added 21 Oct 2026, published 19 Oct 2026
This paper is a controlled, large-scale evaluation of biomedical Retrieval-Augmented Generation across growing corpus sizes. It compares abstract, flat full-text and hierarchical full-text representations, with and without reranking, on multiple-choice QA over five external benchmarks and a held-out test set. RAG gave mean gains of up to 9.6 points externally and 10.2-30.3 points on the held-out set. Hierarchical indexing used 67% fewer units and 44-56% less storage than flat full-text.
RAG
arXiv
research, llm-apps, evalsยท Added 21 Oct 2026, published 19 Oct 2026
RAGLeak is a supply-chain privacy backdoor attack on retrieval-augmented generation. When triggered, the backdoored generator reversibly obfuscates retrieved private documents, and an auxiliary LLM recovers them. Tested on three model families and three domains, it reaches up to 100% attack success rate, loses at most 10 points under detection-based defenses, and survives clean fine-tuning and changes of model scale and version. Source code is released.
LLM
RAGLeak
AI Security, Supply Chainยท Added 21 Oct 2026, published 19 Oct 2026
ThetreStageAI is a retrieval-augmented framework for structured Bengali theatre production planning. It keeps separate scene, actor-blocking and lighting views, embeds Bengali text with BGE-M3, indexes it in view-specific Qdrant collections, and has Gemini generate schema-constrained JSON, with validation and at most one repair attempt. The authors build a synthetic dataset of 500 records (1,500 retrieval documents) and compare single-combined retrieval with multi-view retrieval. Code and data are public.
RAGGeminiBGE-M3+1
ThetreStageAI
llm-apps, researchยท Added 20 Oct 2026, published 16 Oct 2026
This paper presents a multimodal retrieval-augmented generation framework for Medical Visual Question Answering. A Gated Multimodal Unit learns a joint query representation from the medical image and the clinical question, so evidence retrieval uses both modalities while the Med-LVLM stays frozen. On benchmark datasets it consistently beats a reference Med-LVLM and stays competitive with state-of-the-art methods. The page gives no dataset names or numbers.
RAGvision-language model
arXiv
research, llm-apps, multimodalยท Added 20 Oct 2026, published 16 Oct 2026
Proposes protecting the retrieval datastore in RAG systems by applying the same random projection to datastore embeddings and query embeddings. This provably preserves query-item semantic similarity while substantially mitigating data extraction attacks. Across multiple RAG architectures and datasets it beats prior differential-privacy-based methods on retrieval and generation quality, with comparable empirical privacy under strong attacks, making it a practical baseline for privacy-preserving RAG.
RAGLLM
arXiv
research, llm-appsยท Added 17 Oct 2026, published 15 Oct 2026
Proposes applying the same random projection to both datastore embeddings and query embeddings in RAG systems. The authors claim this provably preserves query-to-item semantic similarity while substantially mitigating datastore data-extraction attacks. Across several RAG architectures and datasets it reportedly beats prior differential-privacy-based defenses on retrieval and generation quality, with comparable empirical privacy under strong attack models. It is presented as a lightweight baseline for privacy-preserving RAG.
LLM
differential privacydata extraction attacks
AI Securityยท Added 17 Oct 2026, published 15 Oct 2026
Proposes SCOPED (Skill-Decoupled On-Policy Expert Distillation), which trains capability-specific RL experts for vision-language models. It extracts explicit skills from them through reward-guided reflection. During on-policy distillation the skills guide expert supervision but are hidden from the unified student, so the student internalizes the procedural knowledge and needs no explicit skills at inference. Across eight multimodal benchmarks it beats the backbone and mixed-capability RL. Analyses show cross-capability gradient interference in joint RL, and that skill augmentation gives more consistent expert trajectories, more attention to task-relevant visual evidence, and better preservation of expertise.
vision-language model
SCOPEDarXiv
research, training, multimodalยท Added 17 Oct 2026, published 15 Oct 2026
Legal-R1 is an agentic retrieval framework for evidence-based legal question answering that splits reasoning across three agents. A Lawyer agent decides when to retrieve, which legal source to query, what evidence to seek and when it has enough. A Legal Assistant retrieves statutes and precedent cases with authority-aware and diversity-aware selection, and a Judge agent writes the final answer from the full evidence trajectory. Only the Lawyer agent is post-trained, using masked supervised fine-tuning and trajectory-aware reinforcement learning. The paper reports gains over general-purpose, legal-specific and retrieval-augmented baselines on several Chinese legal benchmarks.
RAG
Legal-R1arXiv
research, agents, llm-appsยท Added 14 Oct 2026, published 13 Oct 2026
This paper shows that vision-language-action (VLA) models are highly sensitive to how instructions are phrased: a single-word change (e.g. "stove" vs "hot plate") can shift task success by dozens of points on ฯ0 and ฯ0.5 checkpoints in the LIBERO benchmark, even after rephrase-augmentation finetuning. The authors characterize this with statistically tested single-edit swings and an oracle phrase search, then mitigate it without retraining by having an LLM distill scoring evidence into 10-20 explicit rephrasing rules that rewrite incoming instructions at deployment time, improving frozen ฯ0 by 16-27% relative on held-out tasks and lifting ฯ0.5's in-finetune success from 93.6% to 97.8%.
vision-language model
ฯ0ฯ0.5LIBERO
research, multimodalยท Added 8 Oct 2026, published 7 Oct 2026
RECAST (Routing Evidence through Computation, Access, and Synthesized Tools) reframes retrieval-augmented generation as a sequential decision process: a lightweight RouterLM iteratively selects or formulates primitive/custom operations, a frozen CompilerLM turns custom operations into executable code, and a frozen AnswerLM produces the final answer once evidence is judged sufficient. RouterLM is trained with supervised fine-tuning followed by GRPO, and the system reaches a 75.6% mean success rate across six heterogeneous benchmark families, beating the strongest large-model baseline by 15.9% and a training-free Gemini 3.5 Flash router by 5.0%, with strong zero-shot generalization on three held-out benchmarks. It targets tasks where evidence must be derived through filtering, aggregation, or computation across multiple sources rather than retrieved verbatim.
Qwen3.5GeminiGRPO
Gemini 3.5 Flash
llm-apps, training, researchยท Added 8 Oct 2026, published 7 Oct 2026
This paper introduces Self-Correction Optimization (SCO), a training-free method that improves consistency in interleaved image-text generation from multimodal large language models. SCO treats the classifier-free guidance update as a reference signal and applies minimal corrections under two constraintsโa new-event constraint for temporal consistency and a state-preserving constraint for visual-subject coherenceโavoiding the computational cost of additional training with augmented data. The authors show improvements on interleaved multimodal generation benchmarks and extend the method to video generation tasks like robot manipulation and long-horizon handcrafting.
vision-language model
research, multimodalยท Added 8 Oct 2026, published 7 Oct 2026
This paper runs a mechanism-isolating ablation of four document-structure treatments used in retrieval-augmented generation โ structure-aligned chunking, LLM-generated chunk contexts, heading-path metadata, and hierarchical two-stage retrieval โ under one protocol with matched chunk sizes and a semantically null placebo (shuffled heading paths) to control for the confound that any prepended text perturbs embeddings. Using a coverage-aware nDCG metric and document-clustered bootstrap with Holm correction across 200 Wikipedia Featured Articles (951 queries) and 1,585 QASPER papers (4,303 questions), it finds that structure-aligned chunking with real heading paths beats both contextualized fixed windows and the placebo, naive hierarchical retrieval hurts performance (traced to first-stage section recall failures), and gold structure beats LLM-induced structure on Wikipedia but not QASPER. Effect sizes are small but statistically significant and consistent across corpora, giving RAG practitioners evidence-based guidance on which structure-aware retrieval tricks actually help versus which are placebo effects.
QASPERWikipedia Featured Articles
research, llm-appsยท Added 8 Oct 2026, published 7 Oct 2026
EntroPrefill proposes a Renyi-entropy-guided mechanism for pruning context mid-prefill in retrieval-augmented generation, using sink-isolated regularized head pooling compatible with grouped-query attention to decide which tokens can be safely discarded. It derives a mixture-to-head deletion envelope bounding feasible token removal, a finite-sample observer guarantee valid under adaptive layer selection, and a conditional transformer perturbation bound with explicit Lipschitz constants and a first-token decision-margin corollary, plus a systems-level break-even analysis of KV-transfer payload versus pruning savings. The paper is explicitly theoretical, defining assumptions and formal limits without reporting empirical speedup or accuracy-preservation results.
Transformer
llm-apps, researchยท Added 8 Oct 2026, published 7 Oct 2026
EC-RAG (Event Chain Retrieval-Augmented Generation) is a training-free framework for long-video question answering that organizes video content into an explicit, temporally ordered "event chain" instead of retrieving isolated frames or snippets. It partitions video into semantically coherent segments, fuses multi-modal signals (speech, text, visual cues) at the event level, and retrieves relevant events from the chain to answer queries, working as a plug-and-play addition to existing large video-language models (LVLMs) without extra training. Evaluated on Video-MME, MLVU, and LongVideoBench, it outperforms frame-level retrieval baselines, showing the value of modeling temporal/event structure for long-video understanding.
vision-language model
Video-MMEMLVULongVideoBench
research, multimodal, llm-appsยท Added 7 Oct 2026, published 6 Oct 2026
RAG-PIBench is a benchmark of 4,876 contextual examples for evaluating prompt-injection detectors embedded in retrieved content within Retrieval-Augmented Generation (RAG) systems. The authors use a leakage-aware construction pipeline with frozen train/validation/protected-test splits and compare keyword-based, semantic-reference, TF-IDF, and transformer-based detectors, finding DistilBERT achieves the best protected-test performance (F1 = 0.896, PR-AUC = 0.968) while TF-IDF with SVM or logistic regression remains competitive. It highlights the risk that naive benchmark construction can leak information between splits, inflating reported detector performance for RAG prompt-injection defenses.
DistilBERTTF-IDF
RAG-PIBench
AI Securityยท Added 7 Oct 2026, published 6 Oct 2026
This paper proposes AI-decision checkpoints, explicit moments in business process development where process developers must evaluate whether to adopt, constrain, or reject AI-candidate sub-processes built with LLMs, RAG, or AI agents, based on expected effects on time, cost, quality, flexibility, and legal or organisational constraints. The checkpoints are embedded in a six-module BPM teaching lifecycle covering process modeling, simulation, workflow execution with AI agents, and process mining, piloted via a fictitious customer-onboarding teaching case with formative observations drawn from instructor logs and student artifacts. It addresses a curriculum gap where AI is still treated as a bolt-on rather than a first-class design element in BPM education.
ai-policyยท Added 6 Oct 2026, published 5 Oct 2026
arxiv.orgpaper๐ง AI
