Techyon

Select, Compress, Reinvest: A Controlled Study of Visual-Token Allocation in Long-Video MLLMs

Long-video language models cannot look at every frame: an hour sampled once per second is 3,600 images, and a system keeps only a small fixed slice of that pool. Which frames survive that slice is usu

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DRACO: Fine-Grained Credit Assignment with Dynamic Rubrics for Long-Horizon Agent Training

Reinforcement Learning from Verifiable Rewards works well when a task has a programmatic checker, but most long-horizon agent domains have none. We work in the outcome-blind setting, where ground-trut

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Compile by Training: Turning Natural-Language Specifications into Local Neural Functions

The paper introduces "compile by training," a method that converts natural‑language specifications into reusable neural functions by generating task‑specific examples with teacher models and training a small adapter for …

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Rethinking On-Policy Distillation of Large Language Models II: One Training Example

Researchers investigated on-policy distillation (OPD) for language models at the data-minimal limit, discovering that training on a single query enables continued improvement for hundreds of steps while recovering most

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Gpt 6 astra benchmarks

Reddit users are sharing links to benchmark results for OpenAI's upcoming GPT-6 "Astra" model, reportedly published by The New Stack. The post highlights performance metrics that have sparked discussion in the r/singular…

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Meta AI Released Muse Spark 1.3: An Agentic Coding Model That Uses ~20% Fewer Tool Calls and ~25% Fewer Tokens Than Muse Spark 1.2

Meta AI introduced Muse Spark 1.3, an agentic coding model that reduces tool calls by ~2

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Four major AI models suffer rare overlapping downtime

Service interruptions hit ChatGPT, Claude, Grok, and Gemini practically simultaneously.

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Elevating security, control, and accessibility: Stack Internal 2026.6

In our 2026.6 release, we are shipping updates across administrative security, programmatic API control, developer portal integrations, and platform-wide accessibility—ensuring both your engineers and

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GPT-6 Astra

(No description available)

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The Cheap Model Is Not Cheap Until It Finishes the Trace: Gemini 3.8 & Muse Spark 1.3

Editorial fact policy. Every number below is tagged as either [VENDOR] or [INDEPENDENT]. Vendor numbers are useful evidence, not verdicts. [ABSTAIN] means the source record was not good enough to pub

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Claude Fable 5.1 surpasses human average on SimpleBench

Here are more results: Claude Fable 5.1 86.6% Human Baseline* 83.7% Gemini 3.8 Flash 82.4% Claude Fable 81.9% Muse Spark 1.3 81.8%   submitted by   /u/Profanion  

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Another OpenAI cryptic post 10 minutes ago with the number 6, GPT 6 coming today?

  submitted by   /u/saln1   to   r/singularity [link]   [comments]

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