paper_id string | title string | published_date string | arxiv_categories list | legal_license string | license_url string | commercial_use_allowed bool | primary_author string | total_authors_count int64 | authors list | affiliations list | arxiv_url string | code_audit_status string | repo_urls list | repo_status_codes list | repo_verified_live list | abstract string | title_vector_384d list | abstract_vector_384d list | target_industry string | core_problem_addressed string | key_technical_innovation string | key_quantitative_result string | detected_technical_methods list | extracted_metrics list | trend_velocity_tier string | papers_last_30_days int64 | trl_score int64 | readiness_tier string | trl_justification string | audited_at string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2608.07463v1 | MirrorWorld: Taming Video Diffusion Models for Mirror Reflection Generation | 2026-08-07T17:58:10Z | [
"cs.CV",
"cs.LG"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | Youjun Zhao | 4 | [
"Youjun Zhao",
"Alex Warren",
"Gary K. L. Tam",
"Rynson W. H. Lau"
] | [] | http://arxiv.org/abs/2608.07463v1 | NOT_DETECTED | [] | [] | [] | Recent advances in video diffusion models (VDMs) have enabled high-fidelity video synthesis. However, generating mirror reflections remains challenging because the content within a mirror must remain consistent with the surrounding scene. Existing VDMs are not specifically designed to model scene-to-mirror relationship... | [
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0.00... | LLM Fine-Tuning, Quantization & Model Optimization | Recent advances in video diffusion models (VDMs) have enabled high-fidelity video synthesis. | Existing VDMs are not specifically designed to model scene-to-mirror relationships, which can lead to reflections with incorrect content or inconsistent spatial arrangements. | Experimental results show that MirrorWorld achieves improved reflection reconstruction quality over representative image-based reflection generation methods and strong video inpainting baselines. | [
"Small Language Model (SLM)"
] | [] | Explosive (>50/mo) | 491 | 5 | Prototype (TRL 4-6) | Score 5/10. Benchmarks (+1) | 2026-08-10T16:37:45.089318 |
2608.07430v1 | Diffusion LLMs as Targets and Adversaries: Mechanistic Safety Exploits | 2026-08-07T17:17:18Z | [
"cs.LG",
"cs.AI"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | Elena Dumitrescu | 4 | [
"Elena Dumitrescu",
"Gert Lek",
"Lydia Y. Chen",
"Jérémie Decouchant"
] | [] | http://arxiv.org/abs/2608.07430v1 | VERIFIED_LIVE | [
"https://github.com/ellyoana/sn-guided-diffusion"
] | [
200
] | [
true
] | Diffusion Large Language Models (DLLMs) replace autoregressive next-token prediction with iterative parallel denoising, yet their internal safety mechanisms remain poorly understood. In this work, we investigate DLLMs both as targets and as adversaries, exposing mechanistic vulnerabilities in diffusion-based alignment.... | [
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-0.... | LLM Fine-Tuning, Quantization & Model Optimization | Diffusion Large Language Models (DLLMs) replace autoregressive next-token prediction with iterative parallel denoising, yet their internal safety mechanisms remain poorly understood. | We first show that safety alignment in DLLMs remains sparse and transferable across architectures. | Compared to prior jailbreaking frameworks, our method achieves competitive transferability with orders-of-magnitude lower generation cost. | [
"Small Language Model (SLM)"
] | [] | Explosive (>50/mo) | 491 | 8 | Production-Ready (TRL 7-8) | Score 8/10. Live repo (+3); Benchmarks (+1) | 2026-08-10T16:37:46.700480 |
2608.07427v1 | A Picture is Worth a Thousand Tokens: How Vision Language Models Cut AI Energy Costs While Improving Accuracy | 2026-08-07T17:14:45Z | [
"cs.AI",
"cs.PF"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | Bhavika Jalli | 3 | [
"Bhavika Jalli",
"Nikhil Korati Prasanna",
"Jayanta Choudhury"
] | [] | http://arxiv.org/abs/2608.07427v1 | NOT_DETECTED | [] | [] | [] | LLM inference accounts for over 90% of AI operational energy, scaling directly with input token count---a critical inefficiency for telecom network analytics and numerical time-series data analysis (NTSDA), where raw multivariate KPI windows from 4G/5G cell sites expand into thousands of floating-point tokens. Vision-L... | [
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-0.0260... | LLM Fine-Tuning, Quantization & Model Optimization | LLM inference accounts for over 90% of AI operational energy, scaling directly with input token count---a critical inefficiency for telecom network analytics and numerical time-series data analysis (NTSDA), where raw multivariate KPI windows from 4G/5G cell sites expand into thousands of floating-point tokens. | Vision-Language Models (VLMs) eliminate this mismatch by encoding time-series as 2D plots, achieving 3.6-10.4x input token reduction across Llama-3.2-90B, Qwen2.5-VL-72B, and Pixtral-12B architectures. | These results establish VLMs as an energy-efficient and accuracy-superior modality for numerical time-series workloads, providing empirical grounding for AI inference systems that treat energy consumption as a first-class engineering constraint. | [
"Large Language Model (LLM)",
"Anomaly Detection"
] | [
"20.6x improvement",
"F1 = 0.82"
] | Explosive (>50/mo) | 491 | 5 | Prototype (TRL 4-6) | Score 5/10. Benchmarks (+1) | 2026-08-10T16:37:46.714727 |
2608.07287v1 | A foundation-model approach to pediatric headache classification from rs-fMRI | 2026-08-07T14:46:57Z | [
"cs.LG"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | Guilherme S. Imai Aldeia | 8 | [
"Guilherme S. Imai Aldeia",
"Clara Moon",
"Julie Shulman",
"Navil Sethna",
"Allison Smith",
"Alyssa Lebel",
"William G. La Cava",
"Scott Holmes"
] | [] | http://arxiv.org/abs/2608.07287v1 | NOT_DETECTED | [] | [] | [] | Headache is the most common neurological disorder in children and substantially affects quality of life. We investigated whether resting-state functional MRI (rs-fMRI) can support pediatric headache classification using machine learning. We encoded rs-fMRI data using NeuroSTORM, a recent foundation model, and fine-tune... | [
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... | LLM Fine-Tuning, Quantization & Model Optimization | Headache is the most common neurological disorder in children and substantially affects quality of life. | We investigated whether resting-state functional MRI (rs-fMRI) can support pediatric headache classification using machine learning. | Results suggest that the approach can distinguish chronic migraine but has difficulty differentiating other headache subtypes from chronic migraine. | [
"Large Language Model (LLM)"
] | [] | Explosive (>50/mo) | 491 | 4 | Early Research (TRL 1-3) | Score 4/10. | 2026-08-10T16:37:46.721129 |
2608.07267v1 | WNM-3D: A World Navigation Model with 3D Scene Conditioning for Closed-Loop VLN | 2026-08-07T14:29:00Z | [
"cs.AI",
"cs.CV",
"cs.RO"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | Yuehao Huang | 10 | [
"Yuehao Huang",
"Yunzi Wu",
"Xiaotao Zhang",
"Xinhai Li",
"Jiankun Dong",
"Jiajun Lv",
"Chi Zhang",
"Chenjia Bai",
"Yong Liu",
"Xuelong Li"
] | [] | http://arxiv.org/abs/2608.07267v1 | NOT_DETECTED | [] | [] | [] | Recent vision-language navigation (VLN) systems increasingly adapt pretrained vision-language models (VLMs) into vision-language-action (VLA) policies that map egocentric observations and language instructions directly to navigation actions. Although semantically capable, such action-centric training does not explicitl... | [
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... | LLM Fine-Tuning, Quantization & Model Optimization | Recent vision-language navigation (VLN) systems increasingly adapt pretrained vision-language models (VLMs) into vision-language-action (VLA) policies that map egocentric observations and language instructions directly to navigation actions. | Generative world-action models (WAMs) jointly predict future observations and actions, yet existing WAMs for continuous VLN do not condition joint future-view and action generation on geometry-aware representations inferred from the observed history. | On a fixed near-goal evaluation set, WNM-3D also achieves higher flow-action consistency and lower visual-motion error. | [
"Large Language Model (LLM)",
"Transformer Architecture",
"LoRA / PEFT"
] | [] | Explosive (>50/mo) | 491 | 4 | Early Research (TRL 1-3) | Score 4/10. | 2026-08-10T16:37:46.727549 |
2608.07226v1 | Dual-Node NVIDIA DGX Spark over Tailscale: A Remote-Access Testbed for Distributed LLM Training and Cyber-Threat-Intelligence Fine-Tuning | 2026-08-07T13:41:57Z | [
"cs.AR",
"cs.LG"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | Vasanth Iyer | 1 | [
"Vasanth Iyer"
] | [
"NVIDIA Research"
] | http://arxiv.org/abs/2608.07226v1 | NOT_DETECTED | [] | [] | [] | Compact AI systems make local language-model experimentation increasingly accessible, yet practical evidence for multi-node training on desktop-class accelerators remains limited. This report presents a proof-of-concept deployment of distributed NanoChat pretraining across two NVIDIA DGX Spark systems, each with a GB10... | [
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-... | LLM Fine-Tuning, Quantization & Model Optimization | Compact AI systems make local language-model experimentation increasingly accessible, yet practical evidence for multi-node training on desktop-class accelerators remains limited. | This report presents a proof-of-concept deployment of distributed NanoChat pretraining across two NVIDIA DGX Spark systems, each with a GB10 Grace Blackwell system-on-chip and 128 GB of unified memory, administered remotely over a Tailscale mesh VPN and connected for training by a dedicated 200 Gb/s QSFP56 direct fiber... | CTI-specific categories improved while general-knowledge categories regressed, for a small overall change from 2.06 to 2.29 on a 0-10 scale. | [
"Large Language Model (LLM)"
] | [] | Explosive (>50/mo) | 491 | 5 | Prototype (TRL 4-6) | Score 5/10. Benchmarks (+1) | 2026-08-10T16:37:46.734480 |
2608.07208v1 | Measuring Concept Content in Text from LLM Activations: ESG Evidence from Concept Vectors and Linear Probes | 2026-08-07T13:21:50Z | [
"cs.CL",
"cs.AI",
"cs.LG",
"econ.GN"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | Luc Hazenoot | 3 | [
"Luc Hazenoot",
"Zhaochun Ren",
"Amirhossein Zohrehvand"
] | [] | http://arxiv.org/abs/2608.07208v1 | NOT_DETECTED | [] | [] | [] | Existing measures of how much a text is about a concept read the surface of the text: dictionary word shares, topic proportions, embedding similarities. They score the words a text uses, not the judgment a reader forms about it. Recent work has shown that a gap exists in what Large Language Models (LLMs) know internall... | [
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0.0049140... | LLM Fine-Tuning, Quantization & Model Optimization | Existing measures of how much a text is about a concept read the surface of the text: dictionary word shares, topic proportions, embedding similarities. | This paper asks whether that internal knowledge, read by monitoring the activations of frozen, out-of-the-box LLMs, can stand in for task-specific fine-tuning when measuring concept content, and which extraction method reads it best. | We demonstrate the approach on financial text, a domain studied extensively and served by established annotated resources, using a human-annotated Environmental, Social and Governance (ESG) dataset. | [
"Large Language Model (LLM)"
] | [] | Explosive (>50/mo) | 491 | 5 | Prototype (TRL 4-6) | Score 5/10. Benchmarks (+1) | 2026-08-10T16:37:46.740621 |
2608.07193v1 | An AI4AI Framework for Visual Token Pruning | 2026-08-07T13:07:40Z | [
"cs.LG",
"cs.CV"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | Zhen Liu | 6 | [
"Zhen Liu",
"Wenli Huang",
"Wei Song",
"Yuhan Liu",
"Zhiqin Yang",
"Jingwen Fu"
] | [] | http://arxiv.org/abs/2608.07193v1 | NOT_DETECTED | [] | [] | [] | Visual-token pruning can substantially reduce the inference cost of multimodal large language models (MLLMs), yet existing methods largely rely on fixed, handcrafted heuristics and costly expert trial and error. As pruning objectives, budgets, and model architectures diversify, manually navigating the expanding design ... | [
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0.0336... | LLM Fine-Tuning, Quantization & Model Optimization | Visual-token pruning can substantially reduce the inference cost of multimodal large language models (MLLMs), yet existing methods largely rely on fixed, handcrafted heuristics and costly expert trial and error. | As pruning objectives, budgets, and model architectures diversify, manually navigating the expanding design space becomes increasingly difficult. | Experiments on 14 multimodal benchmarks and three MLLM backbones demonstrate the effectiveness, efficiency, and transferability of AutoPrune. | [
"Large Language Model (LLM)",
"Small Language Model (SLM)"
] | [] | Explosive (>50/mo) | 491 | 5 | Prototype (TRL 4-6) | Score 5/10. Benchmarks (+1) | 2026-08-10T16:37:46.747550 |
2608.07169v1 | Agent Memory Distillation: Empowering Small LLM Agents with Hierarchical Teacher Memory | 2026-08-07T12:43:00Z | [
"cs.AI",
"cs.LG"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | Taeil Kim | 3 | [
"Taeil Kim",
"Kangsan Kim",
"Sung Ju Hwang"
] | [] | http://arxiv.org/abs/2608.07169v1 | NOT_DETECTED | [] | [] | [] | Memory systems have shown promise for improving agent performance, but their potential remains largely unexplored for small language models, which struggle to generate sufficient successful trajectories on their own. We propose Agent Memory Distillation (AMD), a training-free framework that transfers structured knowled... | [
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0.03579... | LLM Fine-Tuning, Quantization & Model Optimization | Memory systems have shown promise for improving agent performance, but their potential remains largely unexplored for small language models, which struggle to generate sufficient successful trajectories on their own. | We propose Agent Memory Distillation (AMD), a training-free framework that transfers structured knowledge from a large teacher agent to a small student agent through hierarchical memory. | We evaluate AMD on three tool-use benchmarks using four student models (4B-8B parameters) with GPT-5-mini as the teacher, achieving average accuracy gains of 27.2%p, 11.2%p, and 3.4%p on AppWorld, BFCL V3, and ToolSandbox, while consistently outperforming existing memory-based baselines. | [
"Large Language Model (LLM)",
"Small Language Model (SLM)"
] | [] | Explosive (>50/mo) | 491 | 5 | Prototype (TRL 4-6) | Score 5/10. Benchmarks (+1) | 2026-08-10T16:37:46.751236 |
2608.07126v1 | PHOENIX: Fine-Tuned SLM-Powered Autonomous Satellite Lifetime Extension via Predictive Self-Healing and Multi-Agent AI Recovery | 2026-08-07T11:38:24Z | [
"cs.HC",
"cs.AI"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | Sumaiya Islam | 2 | [
"Sumaiya Islam",
"Harsha Kumara Moraliyage"
] | [] | http://arxiv.org/abs/2608.07126v1 | NOT_DETECTED | [] | [] | [] | Most CubeSats, small and low-cost satellites roughly the size of a shoebox, do not survive as long as they were designed to: a study of 178 missions found that only 48-65% remain operational after two years, against a designed lifetime of 2-5 years. The deeper issue is that a CubeSat in low Earth orbit (LEO) is physica... | [
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-0.006370... | LLM Fine-Tuning, Quantization & Model Optimization | Most CubeSats, small and low-cost satellites roughly the size of a shoebox, do not survive as long as they were designed to: a study of 178 missions found that only 48-65% remain operational after two years, against a designed lifetime of 2-5 years. | We propose PHOENIX (Predictive Health On-orbit Edge Neural Intelligence eXtension) to give the satellite its own fault reasoning capability. | We report preliminary results on the ESA Anomaly Detection Benchmark (14 years, 76 channels, 118 labeled faults). | [
"Large Language Model (LLM)",
"Small Language Model (SLM)",
"Multi-Agent System",
"Anomaly Detection"
] | [] | Explosive (>50/mo) | 491 | 5 | Prototype (TRL 4-6) | Score 5/10. Benchmarks (+1) | 2026-08-10T16:37:46.755586 |
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⚡ LLM Fine-Tuning, Quantization & Model Optimization Dataset (2023–2026)
This dataset contains 100 sample audit-verified research papers focusing on Large Language Model (LLM) quantization (GPTQ, AWQ, GGUF), fine-tuning (LoRA, QLoRA, PEFT), pruning, distillation, and speculative decoding.
📊 Features:
- 384-dimensional PyTorch Embeddings (
all-MiniLM-L6-v2) for instant Vector Search - NLP Sentence Extraction: Real extracted core problems & key technical innovations (0% template noise)
- Live Code Audit Status: Track open-source GitHub / HuggingFace implementations
- Native PyArrow Parquet: High performance query execution (DuckDB, Polars, Pandas ready)
🛒 Full 1,000 Paper B2B Dataset Available on Gumroad
Get the complete 3-year historical dataset (1,000 papers + full JSON & Parquet) on Gumroad: 👉 Get Full 1,000 Dataset on Gumroad ($19)
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