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Showing 141-160 of 233 claims in topic "reasoning"

reasoning
fact
Neutral
independent

Gemma4 models produce consistently concise outputs when enable_thinking=false, unlike Qwen3.5

Lewis Tunstall
7/30/2026
Confidence: 80%Source
reasoning
Previous
179
opinion
Neutral
independent

Post-training recipes differ between Qwen3.5 and Gemma4 models, producing different reasoning behaviors

Lewis Tunstall
7/30/2026
Confidence: 60%Source
reasoning
opinion
Bearish
academic

LLMs lack strong generalisation ability, which explains their strange mix of capability and stupidity

AI Alignment Forum
7/29/2026
Confidence: 75%Source
reasoning
opinion
Neutral
academic

We are still where we were in 2022 - on the verge of AGI but not there yet

AI Alignment Forum
7/29/2026
Confidence: 70%Source
reasoning
fact
Neutral
academic

Subject matter experts get much more out of LLMs than amateurs due to LLMs lacking strong generalisation

AI Alignment Forum
7/29/2026
Confidence: 80%Source
reasoning
fact
Neutral
journalist

Leading AI companies believe they could be close to automating AI research

swyx & Alessio
7/29/2026
Confidence: 70%Source
reasoning
fact
Bullish
lab researcher

A new AI system (likely referring to an OpenAI model version 5.6) has solved another longstanding open problem in probability

Greg Brockman
7/29/2026
Confidence: 80%Source
reasoning
critique
Bearish
critic

Current commercial large language models perform worse at chess than a 7-year-old Kasparov would have

Gary Marcus
7/29/2026
Confidence: 85%Source
reasoning
critique
Bearish
critic

Claims of AGI smarter than the smartest humans are false based on LLM chess performance

Gary Marcus
7/29/2026
Confidence: 90%Source
reasoning
critique
Bearish
critic

Off-the-shelf LLMs fail to follow the rules of chess, demonstrating they lack world models

Gary Marcus
7/29/2026
Confidence: 85%Source
reasoning
critique
Bearish
critic

LLMs cannot learn chess without massive data except by relying on purpose-built external tools

Gary Marcus
7/29/2026
Confidence: 80%Source
reasoning
opinion
Bearish
critic

It is unwise to trust systems that cannot learn chess without massive data to run wars

Gary Marcus
7/29/2026
Confidence: 90%Source
reasoning
opinion
Bullish
academic

On-policy self-distillation is a promising approach that removes the need for external teachers required by traditional on-policy distillation.

Computer Vision
7/29/2026
Confidence: 70%Source
reasoning
fact
Bullish
academic

Visual Contrastive Self-Distillation (VCSD) can perform on-policy self-distillation without requiring privileged answers or visual evidence, using only input conditioning through image-content removal.

Computer Vision
7/29/2026
Confidence: 80%Source
reasoning
fact
Bullish
academic

Cross-modal on-policy distillation can transfer reasoning capabilities from text teachers to audio-language students, extending beyond textually recoverable content to reasoning grounded in non-linguistic events, prosody, and conversation

Machine Learning
7/29/2026
Confidence: 75%Source
reasoning
fact
Neutral
academic

Large audio-language models still lag behind text-based large language models in deep logical reasoning, primarily due to the scarcity of high-quality audio reasoning data

Machine Learning
7/29/2026
Confidence: 85%Source
reasoning
fact
Bullish
lab researcher

Non-invasive fine-tuning interventions for efficient reasoning can maintain behavior very similar to the original checkpoint while improving efficiency

Thomas Wolf
7/29/2026
Confidence: 75%Source
reasoning
prediction
Bullish
lab researcher

Fine-tuning LLMs for efficient reasoning with non-invasive interventions that preserve original model behavior could become a standard technique in the field, similar to how quantization has become standard

Thomas Wolf
7/29/2026
Confidence: 60%Source
reasoning
fact
Neutral
unknown

In Memoir's coupled architecture, read-only pondering outperforms coupled pondering on procedural associative recall tasks with key interference

Neural and Evolutionary Computing
7/28/2026
Confidence: 90%Source
reasoning
fact
Bullish
unknown

Both coupled and read-only pondering arms eventually reach perfect recall (1.0000) after 960 training steps

Neural and Evolutionary Computing
7/28/2026
Confidence: 95%Source
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Last synthesis: 2026-09-20. 8,954 pending.