letting models think longer can decrease, not improve, accuracy "Inverse Scaling in Test-Time Compute" this paper shows that increasing inference steps produces an “inverse‑scaling” effect, longer CoT amplify distraction, framing over‑fit, deductive focus loss & more
@askalphaxiv
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Energy-Based Transformers: Novel AI Learning Through Energy Minimization
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potentially a new AI generation paradigm? "Energy-Based Transformers are Scalable Learners and Thinkers" proposes Energy-Based Transformers (EBT), a new AI model that repeatedly checks and improves its own predictions through learned energy minimization.
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Energy Minimization vs Diffusion: Model Optimization Techniques
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But what is "Energy minimization"? Energy minimization directly tweaks a single candidate solution until the model’s energy score can’t get any lower, ending in the one state it deems best. In hindsight, it may sound similar to diffusion, but in contrast, diffusion starts with
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Trilinear Attention Rewrites Transformer Scaling Laws
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what if attention operated in 3D? This paper introduces trilinear (2-simplicial) attention, and it might have just rewrite the current transformer scaling law by squeezing out the same accuracy with far fewer tokens.
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Semantic-Guided Diverse Decoding for LLM
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Semantic-guided Diverse Decoding for LLM This paper present SemDiD, a decoding trick that steers each beam into a different part of the model’s semantic space and keeps them apart while checking basic probability thresholds, so you can end up with good and diverse answers.
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Math Reasoning Improvement Generalizes Across LLM Domains
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benchmark-maxxing math evals can now be spotted systematically In the paper “Does Math Reasoning Improve General LLM Capabilities?” the authors show that models tuned with RL on math data can generalize their gains across domains, while SFT-tuned rarely transfer beyond math,
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Join Us at ICML Conference in Vancouver
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Come join us at ICML in Vancouver! https://
x.com/ElevateHQ_/sta
/ElevateHQ_/status/1940375641494520301
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LLMs Understanding Concepts: Potemkin Intelligence or Real Comprehension
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Can LLMs truly understand concepts or just fake it well enough to pass tests? Researchers found potemkin understanding Models define concepts correctly 94% of the time, but fail to apply them 40-55% of time AI benchmarks may be fundamentally flawed for measuring understanding
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LLM Research Ideas Less Novel Than Human-Generated Ones Study
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LLMs can generate research ideas that look more novel than humans’, but are they actually better? Stanford ran a study where LLM- or human-authored ideas were tested Human ideas were blindly rated consistently better, with LLM ideas seeing 37× larger score drops post-execution
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Chain-of-Thought Reasoning: Interpretability Illusion in AI Models
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Is Chain-of-Thought just an Illusion of Reasoning? New research shows that 25% of AI papers treat CoT as 'interpretable' – but those step-by-step explanations often don't reflect what models actually compute. The reasoning you see ≠ the reasoning that happens