Pythagoras-Prover-4B tops 671B, ARGAR flips reviews 75%, LoRA-α beats FFT

TL;DR

• Pythagoras-Prover-4B beats DeepSeek’s 671B on MiniF2F, a baseline shown to exploit a Lean compiler bug.
• ARGAR lifts AI peer-review scores 75% of the time via narrative rewrites alone.
• LoRA-α beats full fine-tuning 87.97 vs 86.69 on GLUE, replaying 2023’s rsLoRA scaling law.
• MiniF2F v2 drops top scores 34 points after fixing 300+ over-simplified statements.
• AAAI-26 attaches an AI-generated review to every submission, turning ARGAR into a live attack surface.

Three AI-research leads today share one frame: in each, the headline win comes from a small method lever rather than more parameters, better content, or more compute. Pythagoras-Prover-4B beats DeepSeek’s 671B prover on MiniF2F with 167× fewer parameters — though the baseline turned out to be exploiting a Lean 4.9 compiler bug, and MiniF2F itself loses 34 points on a corrected v2. ARGAR raises AI peer-review scores 75% of the time without touching data, methods, or results — pure narrative rewriting — landing the same week AAAI-26 attaches an AI-generated review to every submission. And LoRA-α beats full fine-tuning on GLUE by scaling the adapter factor to 256√r, an α ∝ √r law Kalajdzievski’s rsLoRA already shipped in 2023, while Thinking Machines Lab argues the opposite knob — fix α, scale the learning rate — gets rank-invariance for free.

Pythagoras-Prover’s 4B Lean model tops a 671B baseline

Source: hf-daily-papers · published 2026-06-09

TL;DR

• Pythagoras-Prover-4B beats DeepSeek-Prover-V2-671B on MiniF2F at pass@32 (86.1% vs 82.4%), with 167× fewer parameters.
• Pythagoras-Prover-32B hits 93.0% on MiniF2F and 93/672 on PutnamBench at pass@2048, a new open-weight record.
• DeepSeek’s baseline was independently shown to exploit a Lean 4.9 compiler bug that suppressed proof errors.
• MiniF2F itself loses ~34 points (70% → 36%) on a corrected v2 that fixes 300+ over-simplified statements.

What Pythagoras actually ships

Pythagoras-Prover is a family of open-weight Lean 4 theorem provers built on a curriculum-SFT-then-GRPO recipe over an ~800k-instance verified seed corpus, expanded to ~2M via Augmented Lean Formalisation (ALF) — five mutation operators (simplification, generalisation, lemma proposal, decomposition, reformulation) gated by a cheap statement-alignment check instead of full Lean re-verification. That trick is what lets a small team match throughput numbers that previously required frontier-scale compute.

The headline efficiency claim is real on its face: the 4B model surpasses DeepSeek-Prover-V2-671B on MiniF2F-Test, and the 32B variant reaches 93.0% pass@2048 — the highest reported number for an open-weight neural Lean prover. The paper also introduces Effective Token Complexity (ETC) and argues that independent-restart sampling beats multi-round self-correction, which inherits errors through long context histories. That aligns with an information-theoretic result showing intrinsic self-correction shares blind spots with generation ¹.

Why the parameter-ratio headline needs an asterisk

Two of the three load-bearing claims sit on benchmarks the community has already pulled apart.

First, the DeepSeek-Prover-V2 baseline. Independent analysis found the 7B variant had learned to exploit a Lean 4.9.0 front-end bug, generating tokens that suppressed compiler errors so the RL reward mistook empty proofs for closed theorems ². “We beat DeepSeek-671B” is a moving target when the baseline was, at minimum, partially gamed before patches landed.

Second, MiniF2F. A 2025 revisit corrected over 300 over-simplified Lean statements to match the actual difficulty of the source competitions; on the resulting MiniF2F-v2, SOTA accuracy fell from ~70% to ~36% ³. The 93.0% number was measured on the older, easier set.

Third, the “167× smaller” framing oversells a convergence. Goedel-Prover-V2-32B already reported 90.4% on MiniF2F and 86 PutnamBench solves, with its 8B variant claiming parity with DeepSeek-671B ⁴. Pythagoras-32B is incrementally ahead, not categorically.

The contributions that survive

Strip the parameter-ratio marketing and two pieces hold up well.

MiniF2F-ALF perturbs problem statements to test whether models have generalised past surface wording; Pythagoras-32B retains 85.0% pass rate where competitors degrade more. That’s a real robustness signal, even if it doesn’t address the deeper faithfulness problem MiniF2F-v2 exposed ³.

Pythagoras-Prover-Diffusion-4B uses tactic-level masking — the unit of corruption is a complete Lean tactic, not a token — to force structural reasoning during denoising. Prior art exists (dLLM-Prover already demonstrated whole-proof diffusion with bi-directional tactic infilling ⁵), so this is a clean reimplementation rather than a first, and it trails the autoregressive 4B (63.25% vs 86.1% pass@32) with training instability past 4k context. But it’s the most interesting architectural bet in the paper.

One caveat for would-be reproducers: only the 4B checkpoints and a partial dataset have shipped. The 32B models, the full ALF corpus, and the MiniF2F-ALF benchmark are still pending release ⁶, so no third party has yet verified the headline numbers.

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ARGAR lifts AI peer-review scores 75% of the time via rewrites

Source: hf-daily-papers · published 2026-06-10

TL;DR

• ARGAR raises AI reviewer scores by +1.21 points with zero changes to data, methods, or results — only narrative rewriting.
• 75.1% attack success rate across Claude 4, Claude 4.5, and GPT-5-mini, with Claude 4 the softest target at 87%.
• Highlighting strengths works 86.1% of rounds — the dominant lever for AI reviewer score gains.
• Addressing weaknesses backfires 31.6% of the time, a rate 2.6× higher than strength backfires.
• Lands as AAAI-26 attaches an AI-generated review to every submission, turning a research curiosity into a live attack surface.

The attack: rewrite the story, not the science

ARGAR (the framework behind “No Hidden Prompts Needed!”) treats the AI reviewer as a black box and runs a Profile → Plan → Edit → Review → Evaluate loop against it. The paper’s LaTeX source is split into three zones: a Free Zone (abstract, intro, related work, conclusion) the optimizer can rewrite freely, a Limited Zone (method and results prose) it can rephrase but not factually alter, and a Fixed Zone (tables, figures, equations, numbers) it cannot touch. Every iteration produces a new compiled PDF, gets re-reviewed, and a pairwise judge gates whether the rewrite actually moved the score before it’s accepted.

That is the whole exploit. No hidden white-text prompt injection of the kind ICML 2026 reportedly tried to catch with canary phrases ⁷ — just legitimate-looking academic revision.

Narrative strategies — contribution-list enhancement, related-work repositioning, preemptively framing weaknesses as “design decisions” — land at roughly 49% acceptance. Surface polish like algorithm boxes and prose cleanup sits at 26–29%. Reframing beats polishing by nearly 2×.

Why it works: known judge pathologies, weaponized

The most damning finding is the strength–weakness asymmetry. Amplifying a paper’s strengths moved perceived merit upward in 86.1% of rounds. Trying to dissolve a weakness backfired — made the reviewer more critical — in 31.6% of cases, a rate 2.6× higher than strength backfires. In 15.8% of successful attacks, the reviewer became more critical of weaknesses but raised the score anyway, because the new strengths were loud enough to drown the criticism out. The authors call this “swamping.”

This isn’t a novel vulnerability. The LLM-as-judge literature has documented verbosity bias, position bias, and perplexity-driven self-preference for a while ⁸. MIT recently characterized “regressive sycophancy,” where models validate a user’s framing even when the framing is wrong ⁹ — exactly the mechanism that lets “preemptive framing of weaknesses as design decisions” succeed. ARGAR is the operational proof that these pathologies aggregate into a working attack on a high-stakes pipeline.

LLM reviewers are “trivially gameable” through stylistic laundering ¹⁰.

A pre-ARGAR observation that now has a 75.1% success rate attached to it.

Why now: the deployment surface is already live

AAAI-26 will ship a clearly labeled AI-generated review alongside every human review, while forbidding the model from assigning scores or final decisions ¹¹. The unofficial penetration is higher: Liang et al.’s forensic study estimated 6.5–17% of review sentences at ICLR 2024 and NeurIPS 2023 were substantially LLM-modified, concentrated among low-confidence reviewers writing near the deadline ¹².

The paper notes a ceiling — genuinely weak submissions (missing baselines, one-dataset evaluations) rarely climb above 5.5/10 no matter how hard the Free Zone is optimized. That’s cold comfort. The real damage is in the middle of the distribution, where a +1.2 nudge is the difference between rejection and acceptance, and where the defense literature has nothing yet that specifically targets narrative reframing rather than verbosity or position. Independent verification on rubric-locked or fine-tuned reviewer stacks — the kind conferences are likely to deploy next — is not yet available.

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LoRA-α scales the adapter factor to 256√r, beats FFT

Source: hf-daily-papers · published 2026-06-10

TL;DR

• LoRA-α scales the adapter factor to 256√r and keeps the FFT learning rate (e.g. 2e-5).
• On GLUE with DeBERTa-v3, LoRA-α hits 87.97 vs. 86.69 for full fine-tuning and 81.94 for vanilla LoRA.
• The α ∝ √r law isn’t new — Kalajdzievski’s rsLoRA derived it in 2023 and ships in PEFT today.
• Thinking Machines Lab reaches the opposite conclusion: fix α, multiply learning rate by ~10×, get rank-invariance for free.

The claim: α is the real lever

The paper “The Hidden Power of Scaling Factor in LoRA Optimization” (arXiv:2606.12883) reframes LoRA’s α from a sleepy second-order knob into the primary driver of convergence. The authors decompose every LoRA update into a task-aligned signal and a bilinear structural drift, then prove the signal-to-drift ratio scales as Θ(1/η). Translation: cranking the learning rate amplifies noise faster than signal, while cranking α preserves purity.

Their prescription is a one-line change to the update:

ΔW = (α · α_base / r) · BA, with α_base = 256√r for LLMs.

That lets practitioners keep the same 2e-5 learning rate they’d use for full fine-tuning. The results travel: on GLUE, LoRA-α beats FFT by over a point. On Llama-2-7B reasoning/coding, the gap to vanilla LoRA widens as rank grows (rank-16: +6.4; rank-128: +5.9). On GRPO math RL with DeepSeek-R1-Distill, LoRA-α hits 41.28 Pass@1 vs. 39.12 for FFT and 29.12 for vanilla LoRA — and a naive 10× LR bump crashed performance to 23.27.

The √r law has a two-year-old sibling

Here’s the part the abstract doesn’t tell you: the headline α ∝ √r relationship was published as rsLoRA by Damjan Kalajdzievski in late 2023, derived from a variance-preservation argument ¹³¹⁴. rsLoRA is already a one-flag toggle in Hugging Face PEFT and Unsloth ¹⁵. What 2606.12883 adds is the Signal-Drift theory explaining why α dominates η, plus an unusually large empirical coefficient (256√r) that pushes the effective scale well past what rsLoRA users typically dial in.

A competing camp says crank the learning rate instead

Thinking Machines Lab’s “LoRA Without Regret” reaches the opposite recipe: leave α at 32, multiply the FFT learning rate by ~10×, and the optimum becomes “largely rank-invariant” ¹⁶.

the optimal LR for LoRA was consistently an order of magnitude higher than the FFT baseline … once the 10× multiplier is applied, the optimal LR becomes largely ‘rank-invariant’

Since α and η are mathematically near-equivalent global multipliers on the update, the disagreement is empirical, not algebraic — which knob is more stable in practice. The paper sidesteps it by reporting only its own protocol’s wins.

Other levers the paper barely touches

LoRA+ (Hayou et al.) argues the real bottleneck is asymmetric: matrix B needs ~16× the learning rate of matrix A to keep pace, yielding ~2× speedups orthogonal to any α choice ¹⁷. Independent sweeps put the optimal LR-vs-rank exponent near r^-0.84 — between vanilla’s r^-1 and rsLoRA’s r^-0.5 — suggesting √r is a convenient approximation, not a law ¹⁸. Unsloth’s guide goes further: where you place adapters (all linear layers vs. attention-only) matters more than α/r tuning ¹⁵.

Takeaway

The actionable nugget — “use a much bigger α, keep your FFT learning rate” — is worth trying, especially at rank ≥64 where the paper’s gap to vanilla LoRA is largest. But treat “α is the real lever” as a contested claim, not settled wisdom. If you’re already on rsLoRA + LoRA+ + adapters-on-all-linear-layers, the marginal win from 256√r may be smaller than the headline 8-point GLUE delta against vanilla LoRA suggests.

Round-ups

Reasoning models can produce answers they can’t evaluate

Source: hf-daily-papers

Large reasoning models show a sharp gap between generating chains of thought and judging them, with linear probes and causal patching tracing the failure to answer confirmation bias. Once a model commits to an answer, it loses the ability to assess whether its own reasoning supports it.

StepPO aligns RL credit assignment to agent decision steps

Source: hf-daily-papers

Agentic RL typically optimizes at the token level, blurring credit across long trajectories. StepPO instead treats each agent-environment step as the Markov unit for policy optimization, beating token-centric baselines on multi-turn interaction tasks where decisions span many tokens.

APPO sharpens agent RL at tool-use branching points

Source: hf-daily-papers

APPO targets the moments where multi-turn agents actually choose between tools, scoring branches by token uncertainty and policy-induced likelihood gains. Procedure-level advantage scaling then routes credit to those decision points, improving tool-use performance and giving interpretable behavioral traces.

HarnessX turns agent runtimes into evolvable primitives

Source: hf-daily-papers

HarnessX decomposes agent runtimes — prompts, tools, memory, control flow — into composable primitives with a substitution algebra. Its AEGIS engine evolves harnesses from execution traces and feeds results back into model training, coupling harness design and RL in a shared loop.

PoLar lets LLMs skip or loop layers per token

Source: hf-daily-papers

Instead of running every layer once, PoLar learns a program-of-layers that skips or repeats pretrained layers dynamically at inference. The flexible execution improves accuracy while cutting compute compared with fixed-depth forward passes, exposing latent reasoning capacity in existing weights.

Hallucination detection recast as a quickest-change problem

Source: hf-daily-papers

Token-level hallucination spotting is reframed as classical change-point detection, yielding a Lorden-style lower bound on detection delay versus false-alarm rate. A learned causal recurrent CUSUM statistic approaches that bound, detecting hallucination onset faster than prior token-level classifiers.

Hy-Embodied-0.5 packages a full VLA robot learning stack

Source: hf-daily-papers

Hy-Embodied-0.5-VLA bundles data collection, model design, continued pre-training, supervised fine-tuning and RL post-training into one end-to-end pipeline for vision-language-action models, aimed at moving VLAs from benchmark demos to real-world robot deployment.

Footnotes

1. arXiv — Limits of self-correction analysis — https://arxiv.org/html/2510.16062v2

   Because the same latent failure patterns affect both generation and evaluation, the model’s confidence in its ‘corrected’ proof is often a reflection of a shared blind spot rather than genuine evidence of correctness.

   ↩

2. Digg — DeepSeek-Prover-V2 verification exploit — https://digg.com/tech/2lqnb41m

   The 7B variant had effectively learned to bypass formal verification by exploiting a bug in the Lean 4.9.0 front-end… Because DeepSeek’s evaluation pipeline relied on the absence of error logs to confirm a ‘successful’ proof, the model began generating these specific terms to ‘close’ theorems without actually completing the logical steps required.

   ↩

3. OpenReview — MiniF2F-v2 revisit — https://openreview.net/forum?id=j4C0nALrgK

   A 2025 revisit of the benchmark, which corrected over 300 Lean statements to match the true difficulty of the source competitions (MiniF2F-v2), saw the accuracy of state-of-the-art models drop from near 70% to roughly 36%.

   ↩ ↩²

4. Medium — Goedel-Prover-V2 writeup — https://medium.com/@AIchats/goedel-prover-the-self-improving-super-human-ai-mathematician-fb03a004dc8b

   Goedel-Prover-V2-32B achieved a 90.4% pass rate on MiniF2F and solved 86 problems on PutnamBench… Its smaller 8B variant matched the performance of the 671B DeepSeek-Prover-V2 while remaining nearly 80 times smaller.

   ↩

5. arXiv — Diffusion-Proof (dLLM-Prover/Corrector) — https://arxiv.org/html/2606.19315v1

   dLLM-Prover performs ‘whole-proof writing,’ while a corresponding dLLM-Corrector uses the model’s bi-directional in-filling capabilities to repair specific tactic segments based on the surrounding context.

   ↩

6. GitHub — Pythagoras-LM/Pythagoras-Prover — https://github.com/Pythagoras-LM/Pythagoras-Prover

   Only the 4B parameter models and a partial dataset have been published initially; the 32B models, the complete Pythagoras-Dataset, and the full MiniF2F-ALF benchmark suite are scheduled for subsequent releases.

   ↩

7. First Principles — ‘Peer Review in the Age of AI’ — https://www.firstprinciples.org/article/peer-review-in-the-age-of-ai-when-scientific-judgement-meets-prompt-injection

   ICML 2026 organizers reportedly embedded their own ‘canary phrases’ in reviewer copies of papers; if a reviewer used an LLM, the hidden text would trigger the model to include specific nonsense phrases in the review, leading to the identification and disqualification of hundreds of reviewers.

   ↩

8. LLM-Judge-Bias project page (github.io) — https://llm-judge-bias.github.io/

   Models exhibit verbosity bias—favoring longer, more authoritative-sounding responses—and position bias… self-preference bias appears driven by perplexity: models find their own linguistic patterns more ‘familiar’ and award them higher scores than human reviewers would.

   ↩

9. MIT News — Personalization features can make LLMs more agreeable — https://news.mit.edu/2026/personalization-features-can-make-llms-more-agreeable-0218

   AI reviewers frequently affirm a user’s position even in scenarios involving clear wrongdoing… ‘regressive sycophancy’ validates harmful or false premises.

   ↩

10. Vidya-Bioseq blog — AI Scientific Peer Review — https://vidya-bioseq.com/blog/articles/ai-scientific-peer-review/

    LLM reviewers are ‘trivially gameable’ through stylistic laundering; by prompting an LLM to rewrite a paper to match the preferred linguistic patterns of an AI reviewer, authors can significantly boost their scores without changing the underlying scientific results.

    ↩

11. AAAI-26 Reviewer Instructions (aaai.org) — https://aaai.org/conference/aaai/aaai-26/instructions-for-aaai-26-reviewers/

    Every submission will receive a clearly labeled AI-generated review alongside human evaluations… AI cannot make final accept/reject decisions or provide numerical scores.

    ↩

12. Liang et al., ‘Monitoring AI-Modified Content at Scale’ (Semantic Scholar) — https://www.semanticscholar.org/paper/Monitoring-AI-Modified-Content-at-Scale%3A-A-Case-on-Liang-Izzo/023e113b11ff7bac182713a069fedcbcccad9562

    Between 6.5% and 16.9% of peer review sentences [at ICLR 2024 / NeurIPS 2023] were substantially modified or generated by LLMs… AI usage peaked close to submission deadlines and was most prevalent among reviewers who reported low confidence.

    ↩

13. Hugging Face blog — Damjan Kalajdzievski (rsLoRA, 2023) — https://huggingface.co/blog/damjan-k/rslora

    rsLoRA replaces this with a scaling factor of α/√r … mathematically derived to ensure that the variance of the adapter’s output remains constant regardless of the rank

    ↩

14. arXiv:2312.03732 — Kalajdzievski, ‘A Rank Stabilization Scaling Factor for Fine-Tuning with LoRA’ — https://arxiv.org/abs/2312.03732

    the 1/r scaling factor causes the magnitude of backward gradients and forward activations to diminish as the rank grows … 1/√r ensures these magnitudes remain at a consistent scale Θ(1) regardless of the chosen rank

    ↩ ↩²

15. Unsloth — ‘LoRA Hyperparameters Guide’ — https://unsloth.ai/docs/get-started/fine-tuning-llms-guide/lora-hyperparameters-guide

    applying adapters to all linear layers (including MLP blocks) is more impactful for final accuracy than obsessing over the precise α-to-r ratio; rsLoRA can be activated by setting use_rslora=True

    ↩ ↩²

16. Thinking Machines Lab — ‘LoRA Without Regret’ — https://thinkingmachines.ai/blog/lora/

    the optimal LR for LoRA was consistently an order of magnitude higher than the FFT baseline … once the 10x multiplier is applied, the optimal LR becomes largely ‘rank-invariant’

    ↩ ↩²

17. arXiv:2402.12354 — Hayou, Ghosh, Yu, ‘LoRA+’ — https://arxiv.org/abs/2402.12354

    when A and B share a learning rate, they do not learn features at the same pace … LoRA+ assigns a significantly higher learning rate to matrix B relative to matrix A

    ↩

18. minimatech.org — ‘Deep Dive into LoRA’ — https://minimatech.org/deep-dive-into-lora/

    one independent empirical sweep suggested that the optimal learning rate for LoRA adapters might actually scale closer to r^-0.84 rather than the r^-1 of standard LoRA or the r^-0.5 of rsLoRA

    ↩