Your Agent Doesn't Need More Freedom
Agents got good. The hard part now is everything around them. The post Your Agent Doesn't Need More Freedom appeared first on Towards Data Science.
Topic - Edition
Agents got good. The hard part now is everything around them. The post Your Agent Doesn't Need More Freedom appeared first on Towards Data Science.
Benchmarking NumPyro, affine-invariant MCMC, and dynesty against exact ground truth and watching them fail. The post Three Samplers, One Posterior: NUTS vs. Emcee vs. Nested Sampling appeared first on Towards Data…
Real repositories, seeded agent decisions: what 50,400 runs show about wasted turns, patch churn, and which harness rules help The post Coding Agents Can Solve the Task — But How Much Work Did They Waste? appeared first…
Google’s Playground lets people create playable games through text prompts, with Unity Spark set to add more advanced tools. The partnership’s next challenge is helping creators refine their games and reach players who…
A practical guide to strengthening churn, CLV, and other traditional models with text The post How to Leverage NLP for Product Data Science appeared first on Towards Data Science.
AMD’s Helion combines configurable CPU and GPU racks with model routing to address the computing demands of AI agents. Its potential savings depend on deployment costs and real-world performance.
arXiv:2610.13476v1 Announce Type: new Abstract: Representational similarity measures (RSA, CKA, Procrustes) are chosen for the transformations they treat as irrelevant, and all of them take the set of conditions as…
arXiv:2610.13025v1 Announce Type: new Abstract: Self-evolving agents are increasingly used to improve language model systems without human intervention through either weight updates or harness improvements. Existing…
arXiv:2610.12998v1 Announce Type: new Abstract: Creativity benchmarks for large language models (LLMs) vary widely in task and metric design. This raises the question of whether they capture a common notion of…
arXiv:2610.13171v1 Announce Type: new Abstract: Power sampling improves large language model (LLM) reasoning at test time by sampling from a sharpened version of the base model's distribution. Its sampler, PowerMH, is…
arXiv:2610.13121v1 Announce Type: new Abstract: Cyberbullying is a growing concern in online communities, yet existing Bengali corpora remain limited in contextual information, annotation transparency, scale,…
arXiv:2610.13440v1 Announce Type: cross Abstract: Recent accent classification systems can achieve high accuracy in standard in-corpus evaluations. However, when speakers overlap between the training and test sets, such…
arXiv:2610.13447v1 Announce Type: cross Abstract: Speech recognition on low-cost microcontrollers is, in practice, limited to keyword spotting or a fixed list of commands. We present O\'ido, an inference engine that…
arXiv:2610.13257v1 Announce Type: cross Abstract: Cross-tokenizer on-policy distillation (OPD) requires aligning teacher and student sequences before their scores can be compared. Decoded-text alignment provides such…
arXiv:2610.13102v1 Announce Type: new Abstract: Broadcast archives offer scale and diversity for self-supervised learning (SSL) but contain substantial noisy, non-speech, and multilingual content. Prior work typically…
arXiv:2610.13348v1 Announce Type: new Abstract: Large Language Models (LLMs) struggle to answer questions over long interaction histories, where relevant evidence may be scattered across distant sessions. Existing…
arXiv:2610.02529v2 Announce Type: replace Abstract: Syntactic ambiguity poses a persistent challenge for Arabic NLP, particularly in morphologically rich nominal constructions where multiple structu6ral interpretations…
arXiv:2502.01025v4 Announce Type: replace Abstract: Large language models (LLMs) process entire input contexts indiscriminately, which is inefficient when the information required to answer a query is localized within…
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