{"total":415,"papers":[{"id":"url:thinkingmachines.ai/news/learning-to-replicate-expert-judgment-in-financial-tasks","title":"Learning to Replicate Expert Judgment in Financial Tasks (Thinking Machines)","date":"2026-06-30","section":"Finance","quality":null,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/c8b8e4f5970fc13702c823cc6753accda07d80b025c7699faf5e7f7a2f44a022/","source_url":"https://thinkingmachines.ai/news/learning-to-replicate-expert-judgment-in-financial-tasks/","text_available":false},{"id":"arxiv:2312.17742","title":"Learning Vision from Models Rivals Learning Vision from Data","date":"2023-12-28","section":"Data, training, optimization","quality":0.170732,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/71dc1683019904602d643d16348a76b98c556b924ca964e3949d8d6420dc4402/","source_url":"https://arxiv.org/abs/2312.17742","text_available":true},{"id":"arxiv:2404.07143","title":"Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attention","date":"2024-04-10","section":"LLMs: architectures, context, training","quality":-0.055248,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/fcdbb3ceff27039e0eeeeac187ebf2570e2dc8b6f58ed5ee278f4259e88cfab1/","source_url":"https://arxiv.org/abs/2404.07143","text_available":true},{"id":"arxiv:2511.08544","title":"LeJEPA: Provable and Scalable Self-Supervised Learning Without the Heuristics","date":"2025-11-11","section":"Self-supervised learning and vision","quality":0.332039,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/e2c5835a26dabc321e6364380a37c47c10425ee830ec522564b0604733025a94/","source_url":"https://arxiv.org/abs/2511.08544","text_available":true},{"id":"arxiv:2510.04871","title":"Less is More: Recursive Reasoning with Tiny Networks","date":"2025-10-06","section":"Reasoning and the \"physics\" of language models","quality":null,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/4345eebe9dc7e67f82617896772f55f0970971516995c1688b6812f9ebf054f6/","source_url":"https://arxiv.org/abs/2510.04871","text_available":true},{"id":"arxiv:2311.02462","title":"Levels of AGI for Operationalizing Progress on the Path to AGI","date":"2023-11-04","section":"Agents, open-endedness, AGI","quality":-0.309966,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/288b98572ecf9c0b81bcb6d0c89b523e3b71a5b3623301b9e24536f615a07bca/","source_url":"https://arxiv.org/abs/2311.02462","text_available":true},{"id":"arxiv:2502.03387","title":"LIMO: Less is More for Reasoning","date":"2025-02-05","section":"Reasoning and the \"physics\" of language models","quality":null,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/714392528bb842fb3a61cee5367942f697536f0131b0fcac85c6eb8d2212125c/","source_url":"https://arxiv.org/abs/2502.03387","text_available":true},{"id":"arxiv:2604.01754","title":"LiveMathematicianBench: A Live Benchmark for Mathematician-Level Reasoning with Proof Sketches","date":"2026-04-02","section":"Reasoning and the \"physics\" of language models","quality":0.041286,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/bdd465567c85086267b19e2a2b0d404cb89ac243a1b7318f7ef2265a9818ddb6/","source_url":"https://arxiv.org/abs/2604.01754","text_available":true},{"id":"arxiv:2512.15745","title":"LLaDA2.0: Scaling Up Diffusion Language Models to 100B","date":"2025-12-10","section":"LLMs: architectures, context, training","quality":0.154862,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/90a25474fdf076c254ea3ddbbc7ddbc1f2f49fb98065168d66b5ad9d8f7dafaa/","source_url":"https://arxiv.org/abs/2512.15745","text_available":true},{"id":"arxiv:2602.08676","title":"LLaDA2.1: Speeding Up Text Diffusion via Token Editing","date":"2026-02-09","section":"LLMs: architectures, context, training","quality":-0.033511,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/1b6dd6e64e69da1fff43a183af57a467878db80d54f550bceefa049d7bb18878/","source_url":"https://arxiv.org/abs/2602.08676","text_available":true},{"id":"url:lilianweng.github.io/posts/2023-06-23-agent","title":"LLM Powered Autonomous Agents (Lilian Weng)","date":"2023-06-23","section":"Agents, open-endedness, AGI","quality":null,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/91f9ea2353f77a2dbae1a824c75814796e7525fe9205b5116c5883b0fd340c1e/","source_url":"https://lilianweng.github.io/posts/2023-06-23-agent/","text_available":false},{"id":"arxiv:2402.01817","title":"LLMs Can't Plan, But Can Help Planning in LLM-Modulo Frameworks","date":"2024-02-02","section":"Reasoning and the \"physics\" of language models","quality":-0.200767,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/d4b5a3d7cb6b8ad1768f1c0a91042bc53b290daaf00e9972dff5b384f5996db3/","source_url":"https://arxiv.org/abs/2402.01817","text_available":true},{"id":"url:thinkingmachines.ai/blog/lora","title":"LoRA Without Regret (Thinking Machines)","date":"2025-09-29","section":"Post-training","quality":null,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/09bfccf7014ee63d12eddd2c954194cd981ce104398b7b64c98873bf2f997b95/","source_url":"https://thinkingmachines.ai/blog/lora/","text_available":false},{"id":"url:direct.mit.edu/coli/article/doi/10.1162/coli_a_00481/116160","title":"Machine Learning for Ancient Languages: A Survey","date":"2023-09-01","section":"Other","quality":null,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/e5ae6215e799eebcdf49681d88d6d9ed8600d22d0a0ccc220f1a561f834b068e/","source_url":"https://direct.mit.edu/coli/article/doi/10.1162/coli_a_00481/116160/","text_available":false},{"id":"doi:10.1038/s41586-024-07711-7","title":"Maintaining Plasticity in Deep Continual Learning","date":"2023-06-23","section":"Data, training, optimization","quality":0.362714,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/b5d46327415fe38f2b9160b874735530939961a71536f7f1ac4c0dc7a3af3224/","source_url":"https://doi.org/10.1038/s41586-024-07711-7","text_available":true},{"id":"arxiv:2605.05115","title":"Manifold Steering Reveals the Shared Geometry of Neural Network Representation and Behavior","date":"2026-05-06","section":"Representation alignment","quality":null,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/4d9659f959b746810c945d693de7b0e4d1a73aeaa7ec183aaa7b8f68549e3d50/","source_url":"https://arxiv.org/abs/2605.05115","text_available":true},{"id":"url:anthropic.com/news/mapping-mind-language-model","title":"Mapping the mind of a large language model (Anthropic)","date":"2024-05-21","section":"AI safety and consciousness","quality":null,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/edf322b5841fac48b6247e69ebcb8c3a4598b4af752c1d567c0f9392b778738d/","source_url":"https://www.anthropic.com/news/mapping-mind-language-model","text_available":false},{"id":"arxiv:2301.04104","title":"Mastering Diverse Domains through World Models","date":"2023-01-10","section":"Reinforcement learning","quality":0.159126,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/266d59e63d2f705efdc2eb7ad93f5126b7092b4450efd0d8c17099c51574b176/","source_url":"https://arxiv.org/abs/2301.04104","text_available":true},{"id":"doi:10.1038/s41586-023-06924-6","title":"Mathematical discoveries from program search with large language models","date":"2023-12-14","section":"Agents, open-endedness, AGI","quality":0.540107,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/70a6b427d42fd28c9d538648e148a97db2fb2614c7764f3231e53f4bebdce887/","source_url":"https://doi.org/10.1038/s41586-023-06924-6","text_available":true},{"id":"arxiv:2205.13147","title":"Matryoshka Representation Learning","date":"2022-05-26","section":"Retrieval, embeddings, benchmarks","quality":0.591178,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/803baa0a8ea7ba1bc63bee0ce80006bf4f403ca0db16ada95dd8198da9a4a899/","source_url":"https://arxiv.org/abs/2205.13147","text_available":true},{"id":"arxiv:1708.02072","title":"Measuring Catastrophic Forgetting in Neural Networks","date":"2017-08-07","section":"Data, training, optimization","quality":-0.495708,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/358d4d451093ba5234c459f6323da4968d71d8be905b6612f5bae0162e9145f2/","source_url":"https://arxiv.org/abs/1708.02072","text_available":true},{"id":"arxiv:2512.18746","title":"MemEvolve: Meta-Evolution of Agent Memory Systems","date":"2025-12-21","section":"Agents, open-endedness, AGI","quality":0.399186,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/c2161237fe9d9768afaa2880a40019455779801af5fba05be5781c229c18ea86/","source_url":"https://arxiv.org/abs/2512.18746","text_available":true},{"id":"arxiv:2203.08913","title":"Memorizing Transformers","date":"2022-03-16","section":"LLMs: architectures, context, training","quality":0.238047,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/76deb2bc758a8a71e7c5c5ad6f4b45a1527845ec73784adc6f75f77462a596aa/","source_url":"https://arxiv.org/abs/2203.08913","text_available":true},{"id":"arxiv:2601.03192","title":"MemRL: Self-Evolving Agents via Runtime Reinforcement Learning on Episodic Memory","date":"2026-01-06","section":"Agents, open-endedness, AGI","quality":0.457268,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/4f5bbf3318b1fe409e0ef5dad02157180b844de366d194e82ce74bc997912eff/","source_url":"https://arxiv.org/abs/2601.03192","text_available":true}],"next":"https://vinnik-dmitry07.github.io/favorite-papers/agents/catalog-0-10.html"}