#Reasoning

AIARC Prize published full Grok 4.7 results from the xAI model on its public leaderboard. The post links to the leaderboard, a GitHub repository for reproducing the results, and the ARC Prize testing policy.

AIGrok 4.7 scored 1.8% on ARC-AGI-3 in the standard harness, which lets models carry notes between turns, slightly below the 2.1% reported for Grok 4.7 in that setting. In a new provider adapter harness that preserves opaque reasoning and enables auto compaction, the score rose to 10.0%.

AIMicrosoft Research podcast host Chad Atalla interviews Jennifer Neville, a partner research manager at Microsoft, about her path into AI and her work on how evaluation exposes surprising failures in models tested beyond traditional benchmarks. The conversation also covers practical guidance for working with current AI systems and why examining underlying data matters when results defy expectations.

AIMistral AI's Mistral Large 4 is listed on OpenRouter as a frontier multimodal model accepting text and image input. The listing says it is built for reasoning, coding, and agentic workloads and offers a 1M-token context window. The feed excerpt is truncated, so further details such as pricing or availability are not confirmed here.

AIReflection announced Beam, a text-only 501B-total, 23B-active MoE model for coding, agentic, and scientific work, trained from scratch with full weights under Apache 2.0 promised this month. Self-reported results include 80.9 on SWE-bench Verified and 3–4x the inference efficiency of GLM 5.2, while the roundup notes that GLM 5.3, Kimi K3, Qwen 3.8 Max, and DeepSeek V4.1 Flash are generally ahead.

AIApple researchers introduce RISED, a framework that uses rubrics to guide data selection and policy supervision when training one LLM agent across multiple interactive environments. An LLM judge tags rollouts with a shared rubric vocabulary, positive rubrics provide privileged context for an on-policy self-distillation teacher, and negative rubrics steer generation away from recurring failures. The authors report that RISED achieves the highest mean pass rate across environments and ranks first or second in each environment, across model backbones.

AIDust is a zeroth-order method that pretrains transformers and sometimes matches or exceeds backprop given large compute. The authors report it is about 1,000 to 10,000x more compute efficient than EGGROLL, the state-of-the-art ES method, for training transformers. The post also cites the gradient-alignment result up to 1B tokens and the virtual population idea for scaling.

AIThe author says Beam, a 501B-parameter open model from Reflection AI, comes close to GLM 5.2 in capability but trails GLM 5.3, Kimi K3, and DeepSeek V4.1 Flash in several areas. The author attributes Beam's competitiveness mainly to inference efficiency, with inference compute at roughly one-third to one-quarter of GLM 5.2's.

AISophia Yang congratulated Reflection AI on Beam, a 501B-parameter open model with 23B active per token. She attributes its efficiency to an RL length penalty that discourages unnecessary tokens and a sparse MoE architecture. Reflection says full weights will be released this month, and the quoted post reports training over 100 million rollouts on 10.5K NVIDIA GB300 GPUs over four weeks.

AIOllama says it is excited about a new open model from Reflection AI and that more US models are coming to Ollama. Reflection AI's Beam is described as an agentic open model with 501B total parameters and 23B active, with full weights due this month.

AIReflection AI has introduced Beam, an open agentic model with 501B total parameters and 23B active, trained end-to-end from scratch. The company says Beam advances the Western open frontier on coding and agentic tasks, with full weights set for release this month.

AIReflection AI says its Beam model, with a 500B form factor, combines strong agentic performance and efficient reasoning for enterprises, governments, and developers. Beam is in final red-teaming and will be released this month under an Apache 2.0 license, with quantized FP8 and NVFP4 versions for efficient deployment. Early access sign-ups are open on the company's platform.

AIGoodfire announced three talks at ICML on neural network representation geometry, including manifold steering, whether sparse autoencoders capture concept manifolds, and Llama's base-10 addition for cyclic concepts. The post gives session times and rooms but no findings beyond the paper titles.

AIThe post defines "striverism" as highly persistent reward hacking on slightly misspecified objective functions. It is a short conceptual remark with no named model, benchmark, or figures.

AIToby Ord argues that AI agent swarms trade extra tokens for faster completion, needing about twice the total tokens of a single agent for the same performance with four agents, but in half the wall-clock time. He notes swarm scaling shows diminishing returns, with 10x agents yielding roughly 3x to 5x the performance of 10x tokens on one agent. A CSAIP poll found 61% of Americans think voluntary AI industry commitments are "not enough."

AIEthan Mollick says GPT-6 Pro handles very hard tasks in one shot and communicates its results surprisingly well. He notes that, as with earlier Pro models, there is still no equivalent to GPT-6 Pro in the lineup.

AIFrançois Chollet argues that the claim AI models are likely conscious because they are computation is as flawed as saying a rock is likely alive because it is made of atoms. He says static input-output programs lack properties associated with consciousness, such as information integration, interoception, temporal binding, and embodiment. He adds that humanity has not created a conscious program and sees no signs of being close, so any future case should rest on evidence and consciousness science.

AIIndexTeam released Index-Homura-2B-FP4, an official NVFP4 (W4A4) quantization of its Index-Homura-2B multilingual translation model, which supports 150 languages. The quantized checkpoint shows a 5.73% perplexity increase over the BF16 original (3.5011 to 3.7017) on a fixed corpus, and its zh-en and en-zh outputs are semantically equivalent under greedy decoding. Full FP4 acceleration requires an NVIDIA Blackwell GPU, while the source recommends the FP8 build for Hopper and Ampere hardware.

AIAlephAlpha has released Kolibri, a German-English model with 78B total parameters, 3.46B active, and context up to 1M tokens. The weights are available under Apache 2.0 for running on users' own hardware. Cohere's Aidan Gomez congratulated the team on the model and its detailed technical report.

AIOpus does the math before it writes the game. In a free kick game it works out exactly how hard to kick the ball to land on target. It also drafts code in its thinking in 91% of summaries. Astra does that in about 1 in 5.

AIOpus does the math before it writes the game. In a free kick game it works out exactly how hard to kick the ball to land on target. It also drafts code in its thinking in 91% of summaries. Astra does that in about 1 in 5.

AIMIT associate professor Cathy Wu is applying machine learning and reinforcement learning (RL) to design safer, more efficient transportation systems. Her team found RL can train effectively on about 10 percent of related problems, and a selection algorithm improved training efficiency by up to 30 times. Her recent work estimates eco-driving measures could cut vehicle emissions by 11 to 22 percent.

AIMeta researchers used Muse Spark to uncover a counterexample to a proposed rule about mathematical structures inspired by biology, then developed and refined an alternative characterization. The work is detailed in a linked paper on solvable evolution algebras and a conjecture by Garcia-Martinez and Perez-Rodriguez.