{"total":29,"items":[{"citing_arxiv_id":"2605.23258","ref_index":6,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"A Simple Plug-in for Improving Eviction-Based KV Cache Compression","primary_cat":"cs.LG","submitted_at":"2026-05-22T06:00:15+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"VECTOR augments eviction-based KV cache compression with three-way token routing that combines importance scoring and offline regression-based reconstructability estimation to improve quality at high compression ratios.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.21965","ref_index":9,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"SpecHop: Continuous Speculation for Accelerating Multi-Hop Retrieval Agents","primary_cat":"cs.CL","submitted_at":"2026-05-21T03:55:47+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"SpecHop accelerates multi-hop LLM tool use via continuous multi-threaded speculation with asynchronous verification, approaching oracle latency gains and reducing latency up to 40% on retrieval tasks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.20833","ref_index":25,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"MemGym: a Long-Horizon Memory Environment for LLM Agents","primary_cat":"cs.CL","submitted_at":"2026-05-20T07:25:33+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"MemGym unifies agent gyms into a memory benchmark with isolated scoring across tool-use, research, coding, and computer-use regimes plus a lightweight reward model for tractable coding evaluation.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.19932","ref_index":20,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"PEEK: Context Map as an Orientation Cache for Long-Context LLM Agents","primary_cat":"cs.AI","submitted_at":"2026-05-19T14:51:32+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"PEEK maintains a constant-sized context map via a programmable cache policy to give LLM agents persistent orientation knowledge about recurring external contexts, yielding 6-34% gains and lower cost than prior prompt-learning methods.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.19344","ref_index":21,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"Retrieval-Augmented Linguistic Calibration","primary_cat":"cs.CL","submitted_at":"2026-05-19T04:31:38+00:00","verdict":null,"verdict_confidence":null,"novelty_score":null,"formal_verification":null,"one_line_summary":null,"context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.19035","ref_index":32,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"Trustworthy Agent Network: Trust in Agent Networks Must Be Baked In, Not Bolted On","primary_cat":"cs.AI","submitted_at":"2026-05-18T18:57:54+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Argues that trustworthiness in Agent-to-Agent networks requires a new conceptual framework with four design pillars baked in from the beginning, as retrofitting existing single-agent methods is insufficient.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.18535","ref_index":28,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"Beyond Scaling: Agents Are Heading to the Edge","primary_cat":"cs.LG","submitted_at":"2026-05-18T15:18:29+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Personal agents require edge deployment to preserve high-fidelity local context and zero-latency loops, as claimed through three structural shifts away from cloud-centric designs.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.18226","ref_index":24,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"Context Memorization for Efficient Long Context Generation","primary_cat":"cs.CL","submitted_at":"2026-05-18T11:12:45+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Attention-state memory externalizes long prefixes into a lightweight lookup table of precomputed attention states, yielding higher accuracy than standard in-context learning at fixed memory budgets and lower latency than full attention.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.16474","ref_index":11,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"LERA: LLM-Enhanced RAG for Ad Auction in Generative Chatbots","primary_cat":"cs.IR","submitted_at":"2026-05-15T15:39:30+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"LERA is a retrieve-then-generate auction system that refines ad candidate ranking with LLM logits and applies a threshold-aware critical-value payment rule to maintain truthfulness in chatbot ad insertion.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.15315","ref_index":21,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"Context Pruning for Coding Agents via Multi-Rubric Latent Reasoning","primary_cat":"cs.AI","submitted_at":"2026-05-14T18:30:10+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"LaMR decomposes code context pruning into two rubrics using dedicated CRFs, a mixture-of-experts gate, and AST-derived labels to filter noise and often match or beat full-context baselines on coding benchmarks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.14498","ref_index":18,"ref_count":2,"confidence":0.55,"is_internal_anchor":false,"paper_title":"GroupMemBench: Benchmarking LLM Agent Memory in Multi-Party Conversations","primary_cat":"cs.CL","submitted_at":"2026-05-14T07:38:29+00:00","verdict":"UNVERDICTED","verdict_confidence":"MODERATE","novelty_score":7.0,"formal_verification":"none","one_line_summary":"GroupMemBench is a new benchmark exposing that LLM agent memory systems fail on group conversation properties like speaker-grounded tracking and audience-adapted responses, with top systems at 46% accuracy.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.13534","ref_index":5,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"Scaling Retrieval-Augmented Reasoning with Parallel Search and Explicit Merging","primary_cat":"cs.AI","submitted_at":"2026-05-13T13:46:35+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"MultiSearch uses parallel multi-query retrieval plus explicit merging inside a reinforcement-learning loop to improve retrieval-augmented reasoning, outperforming baselines on seven QA benchmarks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.12335","ref_index":17,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"EHR-RAGp: Retrieval-Augmented Prototype-Guided Foundation Model for Electronic Health Records","primary_cat":"cs.IR","submitted_at":"2026-05-12T16:17:03+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"EHR-RAGp is a retrieval-augmented EHR foundation model that employs prototype-guided retrieval to dynamically integrate relevant historical patient context, outperforming prior models on clinical prediction tasks.","context_count":1,"top_context_role":"method","top_context_polarity":"use_method","context_text":"and integrate information scattered across the patient's complete trajectory. The central assumption motivating this work is that retrieval provides a scalable mechanism for incorporating long-range historical context without forcing the model to encode the entire trajectory within its fixed internal state. Given the remarkable success of Retrieval-Augmented Generation [17] (RAG) techniques in Natural Language Processing (NLP) and their ability to leverage external knowledge beyond the training data of the base Large Language Model (LLM) [18, 19, 20, 21], the same concept can be applied for retrieving and integrating historical EHR data. In other words, by considering the patient's historical events as retrievable pieces of information, an EHR foundation model can dynamically"},{"citing_arxiv_id":"2605.10120","ref_index":15,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"MicroWorld: Empowering Multimodal Large Language Models to Bridge the Microscopic Domain Gap with Multimodal Attribute Graph","primary_cat":"cs.CV","submitted_at":"2026-05-11T07:35:55+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"MicroWorld constructs a multimodal attributed property graph from scientific image-caption data and augments MLLM prompts via retrieval to raise Qwen3-VL-8B performance by 37.5% on MicroVQA and 6% on MicroBench.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"Moreover, models fine-tuned on limited domain data often suffer from poor generalization and catastrophic forgetting of broader capabilities [13, 14]. An alternative paradigm is retrieval-augmented generation (RAG), which enriches the input context with relevant external knowledge at inference time, circumventing the need for extensive domain-specific training [15, 16, 17]. Recent studies have reinforced this insight: for specialized scientific domains with scarce instruction-tuning data, augmenting the reasoning context of general-purpose MLLMs can be more effective and robust than fine-tuning [18, 19], as it preserves the model's broad reasoning capabilities while injecting domain-specific knowledge precisely where it is needed."},{"citing_arxiv_id":"2605.09278","ref_index":32,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"EquiMem: Calibrating Shared Memory in Multi-Agent Debate via Game-Theoretic Equilibrium","primary_cat":"cs.AI","submitted_at":"2026-05-10T03:04:12+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"EquiMem calibrates shared memory in multi-agent debate by computing a game-theoretic equilibrium from agent queries and paths, outperforming heuristics and LLM validators across benchmarks while remaining robust to adversarial agents.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"Contributions to the Theory of Games, 2(28):193-216, 1953. 11 [30] Leslie Lamport, Robert Shostak, and Marshall Pease. The byzantine generals problem. In Concurrency: the works of leslie lamport, pages 203-226. 2019. [31] Donghyun Lee and Mo Tiwari. Prompt infection: Llm-to-llm prompt injection within multi- agent systems.arXiv preprint arXiv:2410.07283, 2024. [32] Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al. Retrieval-augmented generation for knowledge-intensive nlp tasks.Advances in neural information processing systems, 33:9459-9474, 2020. [33] Han Li, Yuling Shi, Shaoxin Lin, Xiaodong Gu, Heng Lian, Xin Wang, Yantao Jia, Tao Huang,"},{"citing_arxiv_id":"2605.07129","ref_index":15,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"RRCM: Ranking-Driven Retrieval over Collaborative and Meta Memories for LLM Recommendation","primary_cat":"cs.IR","submitted_at":"2026-05-08T02:07:30+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"RRCM trains an LLM to dynamically retrieve from collaborative and meta memories using group relative policy optimization driven by final top-k recommendation quality.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"RRCM instead treats collaborative evidence as retrievable textual memory within a unified corpus, enabling on-demand access through natural-language queries. Retrieval augmentation and outside information acquisition.Retrieval-augmented generation grounds LLM outputs by selecting relevant documents and is a standard approach to improve factuality and manage context length [15]. In recommendation, retrieval can fetch metadata snippets, reviews, behavioral statistics, or knowledge-graph facts, and has been used to enrich prompts and to mitigate long-tail hallucinations [16, 17]. Nonetheless, these pipelines typically treat retrieval as a separate heuristic stage (retrieve top-k, then generate), which can retrieve unnecessarily and fail to capture"},{"citing_arxiv_id":"2605.06905","ref_index":12,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"Conservative Flows: A New Paradigm of Generative Models","primary_cat":"cs.LG","submitted_at":"2026-05-07T20:06:08+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Conservative flows generate by running probability-preserving stochastic dynamics initialized at data points rather than noise, using corrected Langevin or predictor-corrector mechanisms on top of any pretrained flow model and showing gains on Swiss-roll, ImageNet-256 and Oxford Flowers-102.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.05790","ref_index":25,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"GazeMind: A Gaze-Guided LLM Agent for Personalized Cognitive Load Assessment","primary_cat":"cs.HC","submitted_at":"2026-05-07T07:26:21+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"GazeMind encodes gaze data for LLM reasoning to deliver interpretable, personalized cognitive load predictions that generalize across tasks without fine-tuning and outperform baselines by over 20% on a new 152-person dataset.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.01970","ref_index":42,"ref_count":2,"confidence":0.55,"is_internal_anchor":false,"paper_title":"Trojan Hippo: Weaponizing Agent Memory for Data Exfiltration","primary_cat":"cs.CR","submitted_at":"2026-05-03T17:07:20+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"The paper defines and evaluates Trojan Hippo attacks on LLM agent memory, showing 85-100% success in data exfiltration across backends and reduced rates with defenses at varying utility costs.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.08143","ref_index":15,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"HoReN: Normalized Hopfield Retrieval for Large-Scale Sequential Model Editing","primary_cat":"cs.LG","submitted_at":"2026-05-02T15:51:31+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"HoReN is a parameter-preserving editor that wraps an MLP with a Hopfield codebook memory and scales to 50K sequential edits on ZsRE while maintaining performance above 0.93.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.27043","ref_index":33,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"CL-bench Life: Can Language Models Learn from Real-Life Context?","primary_cat":"cs.CL","submitted_at":"2026-04-29T17:44:32+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"CL-bench Life shows frontier language models achieve only 13.8% average success on real-life context tasks, with the best model at 19.3%.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.26805","ref_index":37,"ref_count":2,"confidence":0.55,"is_internal_anchor":false,"paper_title":"Bian Que: An Agentic Framework with Flexible Skill Arrangement for Online System Operations","primary_cat":"cs.AI","submitted_at":"2026-04-29T15:35:01+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Bian Que is an agentic framework using a unified operational paradigm, flexible Skill Arrangement, and self-evolving mechanism to automate O&M tasks, achieving 75% alert reduction and over 50% MTTR cut in production deployment.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"generation for knowledge-intensive nlp tasks.Advances in neural information processing systems, 33:9459-9474, 2020. [36] Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, and Hannaneh Hajishirzi. Self-rag: Learn- ing to retrieve, generate, and critique through self-reflection. InThe Twelfth International Conference on Learning Representations, 2023. [37] Soyeong Jeong, Jinheon Baek, Sukmin Cho, Sung Ju Hwang, and Jong C Park. Adaptive-rag: Learning to adapt retrieval-augmented large language models through question complexity. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages"},{"citing_arxiv_id":"2604.17866","ref_index":24,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"Latent Abstraction for Retrieval-Augmented Generation","primary_cat":"cs.CL","submitted_at":"2026-04-20T06:26:13+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"LAnR unifies retrieval-augmented generation inside a single LLM by deriving dense retrieval vectors from a [PRED] token's hidden states and using entropy to adaptively stop retrieval, outperforming prior RAG on six QA benchmarks with better efficiency.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.12890","ref_index":29,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"Towards Long-horizon Agentic Multimodal Search","primary_cat":"cs.CV","submitted_at":"2026-04-14T15:40:28+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"LMM-Searcher uses file-based visual UIDs and a fetch tool plus 12K synthesized trajectories to fine-tune a multimodal agent that scales to 100-turn horizons and reaches SOTA among open-source models on MM-BrowseComp and MMSearch-Plus.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.10291","ref_index":23,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"TimeSeriesExamAgent: Creating Time Series Reasoning Benchmarks at Scale","primary_cat":"cs.AI","submitted_at":"2026-04-11T17:15:26+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"TimeSeriesExamAgent combines templates and LLM agents to generate scalable time series reasoning benchmarks, demonstrating that current LLMs have limited performance on both abstract and domain-specific tasks.","context_count":1,"top_context_role":"other","top_context_polarity":"unclear","context_text":"[21] Yaxuan Kong, Yiyuan Yang, Yoontae Hwang, Wenjie Du, Stefan Zohren, Zhangyang Wang, Ming Jin, and Qingsong Wen. Time-mqa: Time series multi-task question answering with context enhancement.arXiv preprint arXiv:2503.01875, 2025. [22] John Patrick Lalor and Pedro Rodriguez. py-irt: A scalable item response theory library for python.INFORMS Journal on Computing, 35(1):5-13, 2023. [23] Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al. Retrieval-augmented generation for knowledge-intensive nlp tasks.Advances in neural information processing systems, 33:9459-9474, 2020. [24] Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman"},{"citing_arxiv_id":"2603.04751","ref_index":9,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"Evaluating the Search Agent in a Parallel World","primary_cat":"cs.AI","submitted_at":"2026-03-05T02:56:42+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Mind-ParaWorld creates parallel worlds with atomic facts to evaluate search agents on future scenarios, showing they synthesize evidence well but struggle with collection, coverage, sufficiency judgment, and stopping decisions.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2601.12538","ref_index":253,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"Agentic Reasoning for Large Language Models","primary_cat":"cs.AI","submitted_at":"2026-01-18T18:58:23+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"The survey structures agentic reasoning for LLMs into foundational, self-evolving, and collective multi-agent layers while distinguishing in-context orchestration from post-training optimization and reviewing applications across domains.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"unseen tools and hierarchy-aware reranking to balance concentration (for single-tool queries) and diversity (for multi-tool queries). 3.3. Agentic Search Single-agent Agentic Retrieval-Augmented Generation (RAG) systems embed reasoning and control into a centralized agent that governs the entire retrieval-generation loop. Unlike traditional RAG pipelines [252, 10, 253] that perform fixed, one-shot retrieval before generation, agentic RAG agents dynamically controlwhen, what, andhowto retrieve based on real-time reasoning needs. This enables the model to adapt retrieval strategies mid-inference, refine its queries, and better integrate evidence from multiple sources. Based on how the agent selects, refines, and integrates retrieved content during reasoning, we categorize single-agent"},{"citing_arxiv_id":"2511.14881","ref_index":22,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"SilverTorch: A Unified Model-based System to Democratize Large-Scale Recommendation on GPUs","primary_cat":"cs.IR","submitted_at":"2025-11-18T20:00:19+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"SilverTorch replaces standalone ANN indexing and filtering with a unified GPU model using a model-based Bloom index and fused Int8 ANN kernel, delivering up to 23.7x throughput and 13.35x cost efficiency gains on industry data.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2507.10722","ref_index":156,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"Bridging Brains and Machines: A Unified Frontier in Neuroscience, Artificial Intelligence, and Neuromorphic Systems","primary_cat":"q-bio.NC","submitted_at":"2025-07-14T18:43:05+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":2.0,"formal_verification":"none","one_line_summary":"A position and survey paper that identifies convergence between neuroscience, AGI, and neuromorphic computing and outlines four key integration challenges.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"formats, topology choices, and traffic-shaping firmware as from innovations in loss functions or preference data. 4.2.5 Retrieval-Augmented Generation Large language models have recently been enhanced by retrieving and integrating external knowledge at gen- eration time, a paradigm known as retrieval-augmented generation (RAG). A seminal architecture by Lewis et al. [156] introduced RAG for knowledge-intensive tasks, combining a neural retriever with a sequence- to-sequence generator trained end-to-end. This approach enabled models to draw facts from Wikipedia in producing answers, achieving state-of-the-art results on open-domain question answering and demonstrating that even relatively compact generators can outperform much larger parametric models when augmented with"}],"limit":50,"offset":0}