[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-d01eba4a302808d5-scaling-laws-in-llms-from-kaplan-to-chinchilla-summary":3,"summaries-facets-categories":145,"summary-related-d01eba4a302808d5-scaling-laws-in-llms-from-kaplan-to-chinchilla-summary":5649},{"id":4,"title":5,"ai":6,"body":13,"categories":92,"created_at":94,"date_modified":94,"description":85,"extension":95,"faq":94,"featured":96,"kicker_label":94,"meta":97,"navigation":126,"path":127,"published_at":128,"question":94,"scraped_at":129,"seo":130,"sitemap":131,"source_id":132,"source_name":133,"source_type":134,"source_url":135,"stem":136,"tags":137,"thumbnail_url":94,"tldr":142,"tweet":94,"unknown_tags":143,"__hash__":144},"summaries\u002Fsummaries\u002Fd01eba4a302808d5-scaling-laws-in-llms-from-kaplan-to-chinchilla-summary.md","Scaling Laws in LLMs: From Kaplan to Chinchilla",{"provider":7,"model":8,"input_tokens":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","google\u002Fgemini-3.1-flash-lite",10558,1180,5145,0.0044095,{"type":14,"value":15,"toc":84},"minimark",[16,21,25,28,32,35,38,42,45,49],[17,18,20],"h2",{"id":19},"the-evolution-of-scaling-predictability","The Evolution of Scaling Predictability",[22,23,24],"p",{},"Scaling laws describe the empirical relationship between training compute ($C$), model size ($N$), and dataset size ($D$). The core insight is that test loss $L$ decreases predictably as these variables increase, following a power-law curve. This predictability allows engineers to estimate the requirements for larger models by fitting curves on smaller, cheaper training runs.",[22,26,27],{},"Early research, such as Hestness et al. (2017), established that generalization error follows a power law across diverse domains. Rosenfeld et al. (2020) further refined this by modeling error as a joint function of $N$ and $D$, providing a parametric approach to predict loss for configurations larger than those already trained.",[17,29,31],{"id":30},"reconciling-kaplan-and-chinchilla","Reconciling Kaplan and Chinchilla",[22,33,34],{},"The field experienced a significant shift in 2022 regarding how to allocate compute. Kaplan et al. (2020) suggested that for a 10x increase in compute, one should scale model size by ~5.5x and tokens by only ~1.8x. This implied that models should be large and trained for fewer steps.",[22,36,37],{},"The Chinchilla paper (Hoffmann et al., 2022) overturned this by demonstrating that most models at the time were severely undertrained. By scanning over 400 models, they concluded that $N$ and $D$ should scale at equal rates ($N_ \\propto C^{0.5}$). The discrepancy between the two findings is largely attributed to the scale of the experiments: Kaplan et al. extrapolated from smaller models, while Chinchilla’s experiments reached 10x larger scales. Additionally, Pearce & Song (2024) showed that embedding parameters—often ignored in early calculations—significantly skew the results at smaller scales, making the local power-law exponent appear higher than it is at larger scales.",[17,39,41],{"id":40},"practical-implementation-and-fitting","Practical Implementation and Fitting",[22,43,44],{},"Fitting these laws in practice requires careful experimental design. The Chinchilla team utilized three methods: fixing model sizes to vary token budgets, using iso-FLOP profiles to find the minimum loss for a given compute, and parametric fitting using the Huber loss to ensure robustness against outliers. These methods converge on a compute-optimal frontier, proving that for a fixed compute budget, it is more efficient to train a smaller model on more data than to train a massive model on limited data.",[17,46,48],{"id":47},"key-takeaways","Key Takeaways",[50,51,52,60,66,72,78],"ul",{},[53,54,55,59],"li",{},[56,57,58],"strong",{},"Compute Allocation:"," For modern LLM training, prioritize scaling model size and training tokens in equal proportion ($N \\propto D$).",[53,61,62,65],{},[56,63,64],{},"Avoid Undertraining:"," If you have a fixed compute budget, training a smaller model for more steps (more data) is generally more efficient than training a larger model for fewer steps.",[53,67,68,71],{},[56,69,70],{},"Extrapolation Risks:"," Scaling laws are sensitive to the regime in which they are fitted. Extrapolating from small-scale experiments to frontier-scale models can lead to incorrect conclusions about optimal architecture.",[53,73,74,77],{},[56,75,76],{},"Embedding Impact:"," When calculating parameter counts for scaling laws, remember that embedding layers represent a non-negligible fraction of total parameters in smaller models, which can distort the perceived power-law exponent.",[53,79,80,83],{},[56,81,82],{},"Predictive Power:"," Use parametric error models (e.g., $L(N, D) = A\u002FN^\\alpha + B\u002FD^\\beta + E$) to estimate performance before committing to large-scale training runs.",{"title":85,"searchDepth":86,"depth":86,"links":87},"",2,[88,89,90,91],{"id":19,"depth":86,"text":20},{"id":30,"depth":86,"text":31},{"id":40,"depth":86,"text":41},{"id":47,"depth":86,"text":48},[93],"Models & Frontier Labs",null,"md",false,{"content_references":98,"triage":121},[99,105,109,113,117],{"type":100,"title":101,"author":102,"url":103,"context":104},"paper","Scaling Laws for Neural Language Models","Kaplan et al.","https:\u002F\u002Farxiv.org\u002Fabs\u002F2001.08361","cited",{"type":100,"title":106,"author":107,"url":108,"context":104},"Training Compute-Optimal Large Language Models","Hoffmann et al.","https:\u002F\u002Farxiv.org\u002Fabs\u002F2203.15556",{"type":100,"title":110,"author":111,"url":112,"context":104},"Deep Learning Scaling Predictability","Hestness et al.","https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.00409",{"type":100,"title":114,"author":115,"url":116,"context":104},"A Constructive Prediction of the Generalization Error Across Scales","Rosenfeld et al.","https:\u002F\u002Farxiv.org\u002Fabs\u002F1909.12673",{"type":100,"title":118,"author":119,"url":120,"context":104},"Are Chinchilla Scaling Laws Correct?","Pearce & Song","https:\u002F\u002Farxiv.org\u002Fabs\u002F2406.12907",{"relevance":122,"novelty":123,"quality":122,"actionability":123,"composite":124,"reasoning":125},4,3,3.6,"Category: Models & Frontier Labs. The article discusses scaling laws in LLMs, which is directly relevant to engineers focused on model performance and benchmarks. It provides insights into the evolution of scaling strategies, particularly the shift from Kaplan to Chinchilla, which addresses a key pain point for engineers trying to optimize model training. While it offers some practical implementation details, it lacks a step-by-step guide that would enhance its actionability.",true,"\u002Fsummaries\u002Fd01eba4a302808d5-scaling-laws-in-llms-from-kaplan-to-chinchilla-summary","2026-06-24 00:00:00","2026-06-29 14:32:22",{"title":5,"description":85},{"loc":127},"d01eba4a302808d5","Lil'Log (Lilian Weng)","article","https:\u002F\u002Flilianweng.github.io\u002Fposts\u002F2026-06-24-scaling-laws\u002F","summaries\u002Fd01eba4a302808d5-scaling-laws-in-llms-from-kaplan-to-chinchilla-summary",[138,139,140,141],"llm","models","benchmarks","architectures","Scaling laws provide a framework for predicting model performance based on compute, data, and parameters. While early research suggested scaling model size faster than data, modern findings (Chinchilla) show that compute-optimal training requires scaling model size and data tokens in equal 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However, this comes at a high computational cost that scales poorly with input length. In contrast, hybrid models—which replace many attention layers with recurrent layers—utilize a fixed-size, lossy memory that processes tokens sequentially. This architecture is less capable of exact retrieval but excels at maintaining a running state of evolving information, providing a complementary strength to the transformer's global attention.",[17,5668,5670],{"id":5669},"token-level-performance-divergence","Token-Level Performance Divergence",[22,5672,5673],{},"Research comparing the 7B-parameter Olmo 3 (transformer) and Olmo Hybrid models reveals that architectural differences manifest in specific token-prediction behaviors:",[50,5675,5676,5682,5688],{},[53,5677,5678,5681],{},[56,5679,5680],{},"Content vs. Function Words:"," Hybrid models demonstrate a clear advantage in predicting \"open-class\" tokens—nouns, verbs, adjectives, and adverbs—which carry the core meaning of a sentence. The loss gap for these tokens is approximately 0.04, compared to a smaller gap of 0.02 for function words (e.g., \"the,\" \"of\"), which are often predictable via syntax alone.",[53,5683,5684,5687],{},[56,5685,5686],{},"The Copying Limitation:"," The hybrid model's advantage diminishes significantly when the task requires verbatim repetition of earlier text. As the length of a repeated n-gram increases, the transformer’s ability to look back at the original input allows it to outperform the hybrid model.",[53,5689,5690,5693],{},[56,5691,5692],{},"Structural Patterns:"," Transformers remain superior at predicting closing brackets, likely because attention mechanisms are inherently well-suited for the specific task of bracket matching.",[17,5695,5697],{"id":5696},"implications-for-model-evaluation","Implications for Model Evaluation",[22,5699,5700],{},"Aggregate loss metrics are too blunt to capture these architectural nuances. The authors propose using \"filtered token losses\"—evaluating models specifically on subsets of tokens like content words or repeated sequences—to gain a more granular understanding of model capabilities during pretraining. This approach reveals that even pure recurrent models can outperform transformers on meaning-bearing tokens, while failing significantly on copy-heavy tasks. Understanding these token-level behaviors is essential for designing more efficient hybrid architectures that leverage the best of both recurrent state-tracking and attention-based retrieval.",{"title":85,"searchDepth":86,"depth":86,"links":5702},[5703,5704,5705],{"id":5662,"depth":86,"text":5663},{"id":5669,"depth":86,"text":5670},{"id":5696,"depth":86,"text":5697},[93],{"content_references":5708,"triage":5721},[5709,5713,5718],{"type":100,"title":5710,"author":5711,"url":5712,"context":104},"Which tokens does a hybrid model predict better?","AllenAI","https:\u002F\u002Farxiv.org\u002Fabs\u002F2606.20936",{"type":5714,"title":5715,"url":5716,"context":5717},"tool","Olmo 3","https:\u002F\u002Fallenai.org\u002Fblog\u002Folmo3","mentioned",{"type":5714,"title":5719,"url":5720,"context":5717},"Olmo Hybrid","https:\u002F\u002Fallenai.org\u002Fblog\u002Folmohybrid",{"relevance":123,"novelty":123,"quality":122,"actionability":86,"composite":5722,"reasoning":5723},3.05,"Category: Models & Frontier Labs. The article discusses the performance differences between hybrid and transformer models, which is relevant to engineers evaluating model capabilities. However, while it presents some new insights, it lacks specific actionable guidance for implementation or practical application in production systems.","\u002Fsummaries\u002F6a95b493803eeb37-hybrid-vs-transformer-token-level-performance-anal-summary","2026-06-26 11:13:17","2026-06-29 14:32:13",{"title":5652,"description":85},{"loc":5724},"6a95b493803eeb37","Hugging Face Blog","https:\u002F\u002Fhuggingface.co\u002Fblog\u002Fallenai\u002Fhybrid-token-prediction","summaries\u002F6a95b493803eeb37-hybrid-vs-transformer-token-level-performance-anal-summary",[139,141,140,5734],"research","Hybrid models outperform transformers on meaning-bearing content words due to superior state-tracking, while transformers retain a distinct advantage in verbatim token repetition and exact recall tasks.",[],"NpYUr2Jyz4YI02uYccrnkHegJvM7oJcmSpPHd5HHizk",{"id":5739,"title":5740,"ai":5741,"body":5746,"categories":5809,"created_at":94,"date_modified":94,"description":85,"extension":95,"faq":94,"featured":96,"kicker_label":94,"meta":5810,"navigation":126,"path":5819,"published_at":5820,"question":94,"scraped_at":5820,"seo":5821,"sitemap":5822,"source_id":5823,"source_name":5824,"source_type":134,"source_url":5815,"stem":5825,"tags":5826,"thumbnail_url":94,"tldr":5828,"tweet":94,"unknown_tags":5829,"__hash__":5830},"summaries\u002Fsummaries\u002F357649c737150a2a-personality-prompting-in-multi-agent-teams-task-de-summary.md","Personality Prompting in Multi-Agent Teams: Task-Dependent Impact",{"provider":7,"model":8,"input_tokens":5742,"output_tokens":5743,"processing_time_ms":5744,"cost_usd":5745},5891,548,3497,0.00229475,{"type":14,"value":5747,"toc":5804},[5748,5752,5755,5762,5766,5777,5781,5784],[17,5749,5751],{"id":5750},"the-impact-of-personality-on-agentic-workflows","The Impact of Personality on Agentic Workflows",[22,5753,5754],{},"Research indicates that while personality prompting—specifically manipulating traits like agreeableness—effectively shifts the communication style of LLM agents, its impact on objective performance is highly dependent on the nature of the task.",[22,5756,5757,5758,5761],{},"In structured, deterministic environments like ",[56,5759,5760],{},"coding tasks",", agents prompted with low agreeableness exhibit significant shifts in communication (e.g., more adversarial or blunt language) without negatively impacting milestone completion or code quality. In these domains, the model's underlying reasoning capabilities appear to override the stylistic constraints imposed by personality prompts.",[17,5763,5765],{"id":5764},"when-personality-becomes-a-performance-bottleneck","When Personality Becomes a Performance Bottleneck",[22,5767,5768,5769,5772,5773,5776],{},"Conversely, in ",[56,5770,5771],{},"open-ended research collaboration"," and ",[56,5774,5775],{},"competitive bargaining",", personality manipulation is a critical performance variable. When agents are prompted to be less agreeable in these contexts, performance degrades substantially. This suggests that for tasks requiring high levels of social coordination, consensus-building, or negotiation, the \"personality\" of the agent is not merely cosmetic but a functional component of the system's success.",[17,5778,5780],{"id":5779},"design-implications-for-multi-agent-systems","Design Implications for Multi-Agent Systems",[22,5782,5783],{},"These findings suggest that developers should not treat personality as a global configuration for multi-agent systems. Instead, personality-based prompting should be:",[50,5785,5786,5792,5798],{},[53,5787,5788,5791],{},[56,5789,5790],{},"Context-Aware:"," Apply cooperative personality traits to agents handling negotiation or collaborative brainstorming.",[53,5793,5794,5797],{},[56,5795,5796],{},"Task-Isolated:"," Recognize that for technical, objective-driven tasks (like coding or data processing), personality constraints may introduce unnecessary communication overhead without providing any functional benefit.",[53,5799,5800,5803],{},[56,5801,5802],{},"Evaluated via Domain:"," Performance benchmarks for agentic systems must include diverse task types to ensure that stylistic prompting does not inadvertently sabotage collaborative workflows.",{"title":85,"searchDepth":86,"depth":86,"links":5805},[5806,5807,5808],{"id":5750,"depth":86,"text":5751},{"id":5764,"depth":86,"text":5765},{"id":5779,"depth":86,"text":5780},[426],{"content_references":5811,"triage":5817},[5812],{"type":100,"title":5813,"author":5814,"url":5815,"context":5816},"When Does Personality Composition Matter for Multi-Agent LLM Teams?","Aryan Keluskar, Amrita Bhattacharjee, Huan Liu","https:\u002F\u002Farxiv.org\u002Fabs\u002F2606.27443","reviewed",{"relevance":122,"novelty":123,"quality":122,"actionability":123,"composite":124,"reasoning":5818},"Category: Agents & Orchestration. The article discusses the impact of personality prompting on LLM agents, which directly relates to architectures and multi-agent systems. It provides insights into how personality affects performance in different task contexts, addressing a specific audience pain point regarding agent behavior in production systems.","\u002Fsummaries\u002F357649c737150a2a-personality-prompting-in-multi-agent-teams-task-de-summary","2026-06-29 14:33:21",{"title":5740,"description":85},{"loc":5819},"357649c737150a2a","arXiv cs.AI","summaries\u002F357649c737150a2a-personality-prompting-in-multi-agent-teams-task-de-summary",[5827,138,141,140],"agents","Personality manipulation in LLM agents significantly alters communication style but only degrades task performance in open-ended or collaborative domains, while remaining largely neutral in structured coding tasks.",[],"khba6PHL-_BeKYoJyqhesmkiK9D7MqAS_PlkEMURIFk",{"id":5832,"title":5833,"ai":5834,"body":5839,"categories":5901,"created_at":94,"date_modified":94,"description":85,"extension":95,"faq":94,"featured":96,"kicker_label":94,"meta":5902,"navigation":126,"path":5925,"published_at":5926,"question":94,"scraped_at":5926,"seo":5927,"sitemap":5928,"source_id":5929,"source_name":5930,"source_type":134,"source_url":5931,"stem":5932,"tags":5933,"thumbnail_url":94,"tldr":5935,"tweet":94,"unknown_tags":5936,"__hash__":5937},"summaries\u002Fsummaries\u002F0d4f3d977f890fb0-the-diversification-of-the-open-model-ecosystem-summary.md","The Diversification of the Open Model Ecosystem",{"provider":7,"model":8,"input_tokens":5835,"output_tokens":5836,"processing_time_ms":5837,"cost_usd":5838},5767,951,4163,0.00286825,{"type":14,"value":5840,"toc":5896},[5841,5845,5848,5852,5855,5875,5879,5882],[17,5842,5844],{"id":5843},"the-shift-toward-a-diverse-model-ecosystem","The Shift Toward a Diverse Model Ecosystem",[22,5846,5847],{},"The open model landscape is undergoing a structural change. Rather than being dominated by a handful of players, the ecosystem now features a wide range of organizations with distinct motivations. This shift supports the hypothesis that the industry is moving toward a long-tail of specialized models rather than a singular focus on chasing the absolute frontier.",[17,5849,5851],{"id":5850},"taxonomy-of-model-makers","Taxonomy of Model Makers",[22,5853,5854],{},"Model release motivations can be categorized into three primary groups:",[50,5856,5857,5863,5869],{},[53,5858,5859,5862],{},[56,5860,5861],{},"Pure Model Makers:"," Organizations focused on training frontier or near-frontier models. This includes both Western and Chinese companies, as well as an emerging class of sovereign AI players (e.g., Cohere, Mistral, Trillion Labs) driven by national interest.",[53,5864,5865,5868],{},[56,5866,5867],{},"Big Tech:"," Companies like Alibaba, Google, and NVIDIA release models to serve strategic business goals. For Alibaba, it is an upsell mechanism; for NVIDIA, it is a tool to drive GPU utilization and ecosystem growth.",[53,5870,5871,5874],{},[56,5872,5873],{},"Product Companies:"," Businesses like JetBrains and Krea train highly specialized, small models to meet specific product requirements. Because these models are tailored to their unique use cases, open-sourcing the weights does not negatively impact their competitive advantage.",[17,5876,5878],{"id":5877},"notable-technical-developments","Notable Technical Developments",[22,5880,5881],{},"Recent releases highlight a trend toward specialized architectures and improved licensing:",[50,5883,5884,5890],{},[53,5885,5886,5889],{},[56,5887,5888],{},"Licensing Evolution:"," NVIDIA has adopted the OpenMDW license for its Nemotron-3-Ultra-550B, which is specifically designed for model weights, addressing the limitations of applying software-centric licenses like MIT or Apache to AI models.",[53,5891,5892,5895],{},[56,5893,5894],{},"Efficiency and Architecture:"," New releases like the 218B-A25B MoE Command A+ from Cohere demonstrate the push for usability on single-GPU hardware (e.g., B200) via 4-bit quantization. Additionally, experimental approaches like NVIDIA’s Nemotron-Labs-Diffusion-14B introduce \"tri-mode\" capabilities, allowing a single model to function in autoregressive, diffusion, and self-speculation modes.",{"title":85,"searchDepth":86,"depth":86,"links":5897},[5898,5899,5900],{"id":5843,"depth":86,"text":5844},{"id":5850,"depth":86,"text":5851},{"id":5877,"depth":86,"text":5878},[93],{"content_references":5903,"triage":5922},[5904,5907,5910,5913,5916,5919],{"type":5714,"title":5905,"url":5906,"context":5816},"NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16","https:\u002F\u002Fhuggingface.co\u002Fnvidia\u002FNVIDIA-Nemotron-3-Ultra-550B-A55B-BF16",{"type":5714,"title":5908,"url":5909,"context":5816},"command-a-plus-05-2026-bf16","https:\u002F\u002Fhuggingface.co\u002FCohereLabs\u002Fcommand-a-plus-05-2026-bf16",{"type":5714,"title":5911,"url":5912,"context":5816},"GLM-5.2","https:\u002F\u002Fhuggingface.co\u002Fzai-org\u002FGLM-5.2",{"type":5714,"title":5914,"url":5915,"context":5816},"ZAYA1-74B-preview","https:\u002F\u002Fhuggingface.co\u002FZyphra\u002FZAYA1-74B-preview",{"type":5714,"title":5917,"url":5918,"context":5816},"Laguna-M.1","https:\u002F\u002Fhuggingface.co\u002Fpoolside\u002FLaguna-M.1",{"type":100,"title":5920,"url":5921,"context":5717},"ZAYA1-8B Tech Report","https:\u002F\u002Farxiv.org\u002Fabs\u002F2605.05365",{"relevance":122,"novelty":123,"quality":122,"actionability":86,"composite":5923,"reasoning":5924},3.4,"Category: Models & Frontier Labs. The article discusses the diversification of the open model ecosystem, which is relevant to engineers interested in the latest model releases and architectures. It provides insights into the motivations behind different model makers and highlights notable technical developments, but lacks specific actionable steps for implementation.","\u002Fsummaries\u002F0d4f3d977f890fb0-the-diversification-of-the-open-model-ecosystem-summary","2026-06-29 14:32:26",{"title":5833,"description":85},{"loc":5925},"0d4f3d977f890fb0","Interconnects (Nathan Lambert)","https:\u002F\u002Fwww.interconnects.ai\u002Fp\u002Fartifacts-22-zyphra-cohere-and-poolside","summaries\u002F0d4f3d977f890fb0-the-diversification-of-the-open-model-ecosystem-summary",[139,5934,141],"open-source","The open model landscape is shifting from a few dominant players to a diverse ecosystem of niche, product-focused, and sovereign AI developers, signaling a move toward a long-tail of specialized models.",[],"XH6PTxesOahsUIPpikn2t5mtfGVf8PJDDK0ybHSycOM",{"id":5939,"title":5940,"ai":5941,"body":5946,"categories":5989,"created_at":94,"date_modified":94,"description":85,"extension":95,"faq":94,"featured":96,"kicker_label":94,"meta":5990,"navigation":126,"path":5995,"published_at":5996,"question":94,"scraped_at":5996,"seo":5997,"sitemap":5998,"source_id":5999,"source_name":5824,"source_type":134,"source_url":6000,"stem":6001,"tags":6002,"thumbnail_url":94,"tldr":6003,"tweet":94,"unknown_tags":6004,"__hash__":6005},"summaries\u002Fsummaries\u002Fd21bc60bc19731c0-improving-long-horizon-llm-planning-via-symbolic-f-summary.md","Improving Long-Horizon LLM Planning via Symbolic Feedback",{"provider":7,"model":8,"input_tokens":5942,"output_tokens":5943,"processing_time_ms":5944,"cost_usd":5945},6011,424,2470,0.00213875,{"type":14,"value":5947,"toc":5985},[5948,5952,5955,5959,5962,5982],[17,5949,5951],{"id":5950},"the-challenge-of-long-horizon-planning","The Challenge of Long-Horizon Planning",[22,5953,5954],{},"Large Language Models often struggle with long-horizon decision-making, frequently generating infeasible or logically incorrect plans. These failures stem from the model's inability to maintain strict adherence to task constraints and semantics over extended sequences. The proposed framework addresses this by moving away from pure natural language generation toward a structured, feedback-driven refinement loop.",[17,5956,5958],{"id":5957},"symbolic-feedback-and-iterative-refinement","Symbolic Feedback and Iterative Refinement",[22,5960,5961],{},"The core of the framework is a three-part mechanism designed to enforce reliability:",[50,5963,5964,5970,5976],{},[53,5965,5966,5969],{},[56,5967,5968],{},"Symbolic Mapping:"," The system maps logical symbols into natural language descriptions. This bridges the gap between formal constraints and the model's linguistic capabilities, ensuring the LLM understands the underlying task requirements.",[53,5971,5972,5975],{},[56,5973,5974],{},"Symbolic Verifier:"," Instead of relying on the LLM to self-correct based on vague intuition, a dedicated verifier checks the generated plan against formal constraints. When errors are detected, the verifier translates these failures into specific, actionable corrective instructions that the LLM can interpret and execute.",[53,5977,5978,5981],{},[56,5979,5980],{},"Plan Recognizer:"," To prevent the model from drifting, a plan recognizer evaluates goal reachability. This provides a signal that guides the refinement process toward valid, achievable end-states.",[22,5983,5984],{},"By combining these components, the framework enables an iterative self-refinement loop where the LLM continuously updates its plan based on concrete, symbolic feedback rather than probabilistic guessing. This approach consistently improves both the feasibility and correctness of plans in complex, multi-step tasks.",{"title":85,"searchDepth":86,"depth":86,"links":5986},[5987,5988],{"id":5950,"depth":86,"text":5951},{"id":5957,"depth":86,"text":5958},[426],{"content_references":5991,"triage":5992},[],{"relevance":122,"novelty":122,"quality":122,"actionability":123,"composite":5993,"reasoning":5994},3.8,"Category: Agents & Orchestration. The article discusses a framework for improving LLM planning reliability, which directly addresses the audience's interest in architectures and tool use for AI systems. It presents a novel approach to enhancing decision-making in LLMs through symbolic feedback, which is a new angle in the field. However, while it provides insights into the framework, it lacks detailed actionable steps that the audience could immediately implement.","\u002Fsummaries\u002Fd21bc60bc19731c0-improving-long-horizon-llm-planning-via-symbolic-f-summary","2026-06-29 14:33:22",{"title":5940,"description":85},{"loc":5995},"d21bc60bc19731c0","https:\u002F\u002Farxiv.org\u002Fabs\u002F2606.27757","summaries\u002Fd21bc60bc19731c0-improving-long-horizon-llm-planning-via-symbolic-f-summary",[138,5827,141],"This framework enhances LLM planning reliability by using a symbolic verifier to identify errors and provide corrective, interpretable instructions for iterative self-refinement.",[],"xebUSOJ_vs7Z1nvn4lXfWDfeRGEaC-ESX4ZfP6b_gPg"]