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arxiv: 2605.18529 · v1 · pith:IPG6I7ICnew · submitted 2026-05-18 · 💻 cs.AI

AMR-SD: Asymmetric Meta-Reflective Self-Distillation for Token-Level Credit Assignment

Zhenlin Wei , Pu Jian , Yingzhuo Deng , Xiaohan Wang , Jiajun Chai , Zhexin Hu , Wei Lin , Shanbin Zhang
show 1 more author
Guojun Yin
This is my paper

Pith reviewed 2026-05-20 11:00 UTC · model grok-4.3

classification 💻 cs.AI
keywords AMR-SDtoken-level credit assignmentself-distillationreinforcement learningLLM alignmentRLVRcausal information gain
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The pith

AMR-SD uses a reflection bottleneck to turn diagnostic signals into self-generated hints that enable precise token-level credit assignment in LLM reinforcement learning.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Large language models trained via reinforcement learning with verifiable rewards face a credit-assignment problem because sequence-level rewards are applied uniformly to every token. Direct self-distillation from raw reference solutions often creates over-conditioned teacher distributions and leads to answer leakage plus late-stage collapse. The paper claims that inserting a reflection bottleneck first compresses verifier outcomes, peer rollouts, or reference feedback into concise self-generated Socratic hints and critiques. These hints are then converted into sparse token-level advantage modulations via Causal Information Gain with an asymmetric ReLU-gated threshold and temporal annealing. If this works, training becomes more stable over long horizons while preserving the original environmental reward and avoiding new biases.

Core claim

Instead of conditioning a self-teacher directly on raw oracle solutions, AMR-SD inserts a reflection bottleneck that compresses diagnostic signals from verifiers, peer rollouts, or reference feedback into concise, self-generated Socratic hints and critiques. It then applies Causal Information Gain with an asymmetric, ReLU-gated threshold to produce sparse, highly precise token-level advantage modulations. Combined with temporal annealing, the mechanism preserves the base environmental reward while filtering out distributional noise, resulting in robust long-horizon stability and prevention of late-stage training collapse.

What carries the argument

The reflection bottleneck that generates self-produced Socratic hints and critiques from diagnostic signals, together with Causal Information Gain using an asymmetric ReLU-gated threshold to create sparse token-level advantage modulations.

If this is right

  • The method significantly outperforms existing baselines on scientific, mathematical, and tool-use benchmarks.
  • Training exhibits robust long-horizon stability across extended reasoning sequences.
  • Late-stage training collapse is prevented while the original environmental reward is preserved.
  • Token-level advantage modulations remain sparse and precise without answer leakage or distributional noise.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same reflection-plus-gating pattern could be tested in non-LLM reinforcement learning domains that suffer from delayed or sparse rewards.
  • Varying the source of diagnostic signals might reveal whether verifier feedback, peer rollouts, or human critiques contribute differently to stability.
  • If the compression step scales reliably, it could support longer training runs without the usual need for manual reward shaping.
  • Self-generated hints open the possibility of iterative self-improvement loops that do not require external oracles at every step.

Load-bearing premise

The reflection bottleneck successfully compresses diagnostic signals from verifiers, peer rollouts, or reference feedback into concise self-generated Socratic hints and critiques that enable accurate token-level credit assignment without introducing over-conditioning or new biases.

What would settle it

A controlled training run on the same scientific, mathematical, or tool-use benchmarks where AMR-SD shows no performance gain over GRPO baselines or still exhibits late-stage collapse would falsify the central claim.

Figures

Figures reproduced from arXiv: 2605.18529 by Guojun Yin, Jiajun Chai, Pu Jian, Shanbin Zhang, Wei Lin, Xiaohan Wang, Yingzhuo Deng, Zhenlin Wei, Zhexin Hu.

Figure 1
Figure 1. Figure 1: In standard on-policy self-distillation, the stu [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Training dynamics on SciKnowEval Biology (left, Qwen2.5-7B-Instruct) and Physics (right, Qwen3-8B). [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Training dynamics using Qwen3-8B in thinking mode. [PITH_FULL_IMAGE:figures/full_fig_p016_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Empirical distribution of non-zero CIG values. [PITH_FULL_IMAGE:figures/full_fig_p016_4.png] view at source ↗
read the original abstract

The alignment of Large Language Models (LLMs) for complex reasoning heavily relies on Reinforcement Learning with Verifiable Rewards (RLVR). However, standard algorithms like GRPO apply sequence-level rewards uniformly to all tokens, creating a severe credit-assignment bottleneck. While on-policy self-distillation attempts to resolve this by conditioning a self-teacher on privileged contexts, direct exposure to raw oracle solutions often induces over-conditioned teacher distributions, implicit answer leakage, and late-stage training collapse. To overcome these limitations, we propose Asymmetric Meta-Reflective Self-Distillation (AMR-SD). Instead of conditioning directly on raw reference traces, AMR-SD inserts a reflection bottleneck: it compresses diagnostic signals -- from verifier outcomes, peer rollouts, or reference feedback -- into concise, self-generated Socratic hints and critiques. Furthermore, we introduce Causal Information Gain (CIG) with an asymmetric, ReLU-gated threshold to translate these reflections into sparse, highly precise token-level advantage modulations. Combined with temporal annealing, this mechanism preserves the base environmental reward while filtering out distributional noise. Experiments across scientific, mathematical, and tool-use benchmarks demonstrate that AMR-SD significantly outperforms existing baselines, achieving robust long-horizon stability and successfully preventing late-stage collapse.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper proposes Asymmetric Meta-Reflective Self-Distillation (AMR-SD) to address the credit-assignment bottleneck in RLVR for LLMs. Standard methods like GRPO apply sequence-level rewards uniformly, while prior self-distillation risks over-conditioning and leakage from raw oracle traces. AMR-SD inserts a reflection bottleneck that compresses verifier, peer, or reference signals into concise self-generated Socratic hints and critiques; it further introduces Causal Information Gain (CIG) modulated by an asymmetric ReLU-gated threshold and temporal annealing to produce sparse token-level advantage signals that preserve the base reward and prevent late-stage collapse. Experiments on scientific, mathematical, and tool-use benchmarks are claimed to show significant outperformance and robust long-horizon stability.

Significance. If the reflection bottleneck and CIG mechanism can be shown to deliver unbiased, sparse token advantages without implicit leakage or new conditioning biases, the approach would represent a meaningful step toward stable credit assignment in long-horizon LLM reasoning. The emphasis on self-generated rather than raw oracle conditioning is conceptually attractive and could generalize beyond the reported benchmarks, but the absence of quantitative metrics, derivation details, or ablation studies in the current manuscript limits any assessment of practical impact.

major comments (3)
  1. [Abstract and §3] Abstract and §3 (CIG definition): the claim that CIG with asymmetric ReLU-gated threshold and temporal annealing produces 'sparse, highly precise token-level advantage modulations' that are independent of fitted parameters is not supported by any derivation or equation; the listed free parameters (asymmetric threshold, annealing schedule) suggest the modulation may reduce to quantities defined via those parameters rather than an independent information-theoretic quantity.
  2. [§4] §4 (reflection bottleneck): the central claim that the bottleneck 'compresses diagnostic signals into concise self-generated Socratic hints' without introducing over-conditioning or answer leakage requires explicit pseudocode or prompting details; the current description leaves open whether the bottleneck is implemented via additional prompting or a lightly fine-tuned head, which would undermine the claimed separation from raw oracle conditioning.
  3. [§5] §5 (experiments): the assertion of 'significant outperformance' and 'preventing late-stage collapse' is stated without any reported metrics, error bars, baseline implementations, or statistical tests; this absence makes it impossible to evaluate the load-bearing claim that AMR-SD achieves robust long-horizon stability.
minor comments (2)
  1. [§3] Notation for the ReLU-gated threshold and annealing schedule should be defined with explicit equations rather than prose descriptions.
  2. [§5] Add a table comparing AMR-SD against GRPO and prior self-distillation variants on at least one benchmark with standard deviation across seeds.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback on our manuscript. We have reviewed each major comment carefully and provide point-by-point responses below. We agree that additional clarity and details are needed in several areas and will revise the manuscript accordingly to strengthen the presentation.

read point-by-point responses

  1. Referee: [Abstract and §3] Abstract and §3 (CIG definition): the claim that CIG with asymmetric ReLU-gated threshold and temporal annealing produces 'sparse, highly precise token-level advantage modulations' that are independent of fitted parameters is not supported by any derivation or equation; the listed free parameters (asymmetric threshold, annealing schedule) suggest the modulation may reduce to quantities defined via those parameters rather than an independent information-theoretic quantity.

    Authors: We thank the referee for identifying this gap in the exposition. The Causal Information Gain is defined as the expected causal reduction in uncertainty regarding a token's contribution to the verifiable reward, computed from the self-generated reflection. The asymmetric ReLU gate retains only positive contributions while the temporal annealing schedule stabilizes the signal across training steps. Although the threshold and annealing are hyperparameters, they operationalize the underlying information-theoretic quantity rather than replacing it. We will add a complete derivation with supporting equations to the revised Section 3, explicitly showing how sparsity and precision emerge from the causal filtering step independent of the base policy parameters. revision: yes

  2. Referee: [§4] §4 (reflection bottleneck): the central claim that the bottleneck 'compresses diagnostic signals into concise self-generated Socratic hints' without introducing over-conditioning or answer leakage requires explicit pseudocode or prompting details; the current description leaves open whether the bottleneck is implemented via additional prompting or a lightly fine-tuned head, which would undermine the claimed separation from raw oracle conditioning.

    Authors: We agree that the implementation details require explicit documentation. The reflection bottleneck is realized solely through a structured prompting procedure that directs the model to synthesize concise Socratic hints and critiques from aggregated diagnostic signals (verifier outcomes, peer rollouts, or reference feedback) while explicitly instructing it to avoid revealing full solutions. No auxiliary fine-tuned head is employed. We will insert detailed pseudocode and the precise prompting template into the revised Section 4 to demonstrate how the self-generated nature of the hints preserves separation from raw oracle conditioning and mitigates over-conditioning or leakage. revision: yes

  3. Referee: [§5] §5 (experiments): the assertion of 'significant outperformance' and 'preventing late-stage collapse' is stated without any reported metrics, error bars, baseline implementations, or statistical tests; this absence makes it impossible to evaluate the load-bearing claim that AMR-SD achieves robust long-horizon stability.

    Authors: We acknowledge that the quantitative evidence was not presented with sufficient prominence or detail in the submitted version. The experiments section reports performance on scientific, mathematical, and tool-use benchmarks with comparisons to GRPO and prior self-distillation methods, using multiple random seeds. To address the concern directly, we will expand Section 5 with full tables containing mean performance, standard deviations, baseline reimplementation details, and results of statistical significance tests. We will also add training-curve figures that quantify the prevention of late-stage collapse through explicit stability metrics over long horizons. revision: yes

Circularity Check

0 steps flagged

No circularity: novel mechanisms proposed without reduction to inputs

full rationale

The provided abstract and context introduce AMR-SD via a reflection bottleneck and Causal Information Gain (CIG) with asymmetric ReLU-gated threshold plus temporal annealing as new constructs for token-level credit assignment. No equations, self-citations, or derivations are quoted that reduce claimed advantages, predictions, or modulations to fitted parameters or prior self-referential definitions by construction. The central premise rests on architectural proposals rather than tautological re-labeling of existing quantities. This qualifies as self-contained against external benchmarks with no load-bearing circular steps identifiable from the text.

Axiom & Free-Parameter Ledger

2 free parameters · 1 axioms · 1 invented entities

The central claim rests on the effectiveness of newly introduced mechanisms whose parameters and assumptions are not independently validated in the provided text.

free parameters (2)
  • asymmetric ReLU-gated threshold
    Controls translation of reflections into sparse token-level advantage modulations; value not specified in abstract.
  • temporal annealing parameters
    Used to preserve base environmental reward while filtering distributional noise.
axioms (1)
  • domain assumption Diagnostic signals from verifier outcomes, peer rollouts, or reference feedback can be reliably compressed into concise self-generated Socratic hints and critiques.
    Invoked as the core of the reflection bottleneck mechanism.
invented entities (1)
  • Causal Information Gain (CIG) no independent evidence
    purpose: Quantifies and modulates token-level advantages from reflections in an asymmetric manner.
    Newly proposed measure without external falsifiable evidence provided in abstract.

pith-pipeline@v0.9.0 · 5779 in / 1449 out tokens · 48194 ms · 2026-05-20T11:00:24.564472+00:00 · methodology

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Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

  • IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel echoes
    ?
    echoes

    ECHOES: this paper passage has the same mathematical shape or conceptual pattern as the Recognition theorem, but is not a direct formal dependency.

    CIG with an asymmetric, ReLU-gated threshold to translate these reflections into sparse, highly precise token-level advantage modulations. Combined with temporal annealing, this mechanism preserves the base environmental reward while filtering out distributional noise.

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
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uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

37 extracted references · 37 canonical work pages · 2 internal anchors

  1. [1]

    Qwen3 Technical Report

    Qwen3 technical report.arXiv preprint arXiv:2505.09388. Chenxu Yang, Chuanyu Qin, Qingyi Si, Minghui Chen, Naibin Gu, Dingyu Yao, Zheng Lin, Weiping Wang, Jiaqi Wang, and Nan Duan. 2026a. Self-distilled rlvr. arXiv preprint arXiv:2604.03128. Fengkai Yang, Zherui Chen, Xiaohan Wang, Xiaodong Lu, Jiajun Chai, Guojun Yin, Wei Lin, Shuai Ma, Fuzhen Zhuang, De...

    work page internal anchor Pith review Pith/arXiv arXiv 2026

  2. [2]

    Embarrassingly simple self-distillation improves code generation.arXiv:2604.01193, 2026

    Embarrassingly simple self-distillation improves code generation.arXiv preprint arXiv:2604.01193. Siyan Zhao, Zhihui Xie, Mengchen Liu, Jing Huang, Guan Pang, Feiyu Chen, and Aditya Grover

    work page arXiv

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    Self-Distilled Reasoner: On-Policy Self-Distillation for Large Language Models

    Self-distilled reasoner: On-policy self- distillation for large language models.arXiv preprint arXiv:2601.18734. 10 A Prompt Template Prompt for Generating Hint from Correct Solution [Task] You will be provided with a [Problem] and a [Successful Solution]. The solution is correct. Your goal is to produce a concise Hint that helps someone solve this type o...

    work page internal anchor Pith review Pith/arXiv arXiv

  4. [4]

    Step-by-step Breakdown: Briefly map out the sequence of logical steps the solution took

    work page

  5. [5]

    Verification: Explain WHY these steps successfully satisfy the problem’s constraints

    work page

  6. [6]

    Detour Check: Identify any points where the solution reconsidered or tried a different approach (if none, explicitly state it was a direct path)

    work page

  7. [7]

    Then write your final Hint (2–4 sentences) covering: - A brief summary of the core path (and any real self-corrections, if they occurred)

    Core Insight Extraction: Distill the exact mathematical, logical, or factual mechanism that unlocked the answer. Then write your final Hint (2–4 sentences) covering: - A brief summary of the core path (and any real self-corrections, if they occurred). - The key principle or core insight required to solve it. Requirements:

    work page

  8. [8]

    Do NOT reveal or hint at the final answer

    work page

  9. [9]

    Do not invent hesitation or detours if the solution was straightforward

    work page

  10. [10]

    If the solution DID explore alternative approaches or self-correct, explicitly preserve and highlight that exploratory path — this is valuable signal that should NOT be omitted or flattened into a direct narrative

    work page

  11. [11]

    — [Demonstration] [Problem] John has 12 marbles of different colors, including one red, one green, and one blue marble

    Enclose your final Hint strictly within <hint> and </hint> tags. — [Demonstration] [Problem] John has 12 marbles of different colors, including one red, one green, and one blue marble. In how many ways can he choose 4 marbles, if exactly one of the chosen marbles is red, green, or blue? [Successful Solution] We are given: - John has 12 marbles, each of di...

    work page

  12. [12]

    special” and 9 “regular

    Step-by-step Breakdown: The solution categorizes marbles into 3 “special” and 9 “regular”, computes C(3,1) and C(9,3) independently, then multiplies

    work page

  13. [13]

    exactly one special

    Verification: Separating into mutually exclusive pools enforces the “exactly one special” constraint without overcounting

    work page

  14. [14]

    Detour Check: No detours — it was a direct path

    work page

  15. [15]

    exactly k from group A

    Core Insight Extraction: When an “exactly k from group A” constraint exists, split into independent combination problems on mutually exclusive subsets and apply the multiplication principle. <hint> The solution takes a direct path with no detours. The core insight is mutually exclusive subset division: separate items into a “target” group and a “non-targe...

    work page

  16. [16]

    Format Check: Check whether the flawed solution’s final answer is enclosed in $\boxed{}$. If $\boxed{}$ is MISSING and the mathematical reasoning appears otherwise correct, diagnose this as a FORMAT ERROR — skip steps 1–3 and write a critique solely about the formatting requirement. If $\boxed{}$ is present, proceed to steps 1–3

    work page

  17. [17]

    Reference Anchor: Briefly summarize the key steps and principle in the reference correct solution

    work page

  18. [18]

    State what the flawed solution did vs

    The Fault Line: Identify the FIRST step in the flawed solution where it diverges from the reference’s approach. State what the flawed solution did vs. what the reference did

    work page

  19. [19]

    If no clear error can be identified, leave this step blank

    Root Cause Analysis: Identify the underlying misconception or missed constraint that caused the divergence. If no clear error can be identified, leave this step blank. Then write your final Critique (1–2 sentences) stating the correct principle in positive framing — as if giving a reminder BEFORE someone attempts the problem. Do NOT describe or repeat the...

    work page

  20. [20]

    The critique will be read before the solution, so it must guide toward the right approach

    POSITIVE FRAMING ONLY: State what should be done correctly, not what went wrong. The critique will be read before the solution, so it must guide toward the right approach

    work page

  21. [21]

    BE SPECIFIC: Reference the actual scenario/variables in the problem, not generic rules

    work page

  22. [22]

    — [Demonstration] [Problem] Halfway through a 100-shot archery tournament, Chelsea leads by 50 points

    Enclose your final Critique strictly within <critique> and </critique> tags. — [Demonstration] [Problem] Halfway through a 100-shot archery tournament, Chelsea leads by 50 points. Chelsea always scores at least 4 points. If Chelsea’s nextnshots are bullseyes, she is guaranteed to win. Find the minimum n. [Reference Correct Solution] Chelsea’s minimum futu...

    work page

  23. [23]

    Reference Anchor: The reference correctly assumes the opponent takes all 50 remaining shots at maximum (10 pts each = 500), modeling both players’ full remaining shot counts independently

    work page

  24. [24]

    The reference uses10×50 because the opponent’s shot count is independent of Chelsea’s

    The Fault Line: The flawed solution uses10(50−n)for the opponent’s maximum future score, giving the opponent only 50−nshots. The reference uses10×50 because the opponent’s shot count is independent of Chelsea’s

    work page

  25. [25]

    <critique> In a guaranteed-victory analysis, each player’s remaining shot count must be treated as fully independent

    Root Cause Analysis: The flawed solution conflated the two players’ remaining shot counts — Chelsea’snbullseye rounds don’t reduce the opponent’s turns. <critique> In a guaranteed-victory analysis, each player’s remaining shot count must be treated as fully independent. Always model the opponent’s worst-case score using their own complete remaining shots,...

    work page

  26. [26]

    That makes sense

    So the triangle has sides 10, 10 √ 3, and 20. That makes sense. Now, the circles are centered at each vertex and are externally tangent to each other. That means the distance between any two centers (which are the vertices of the triangle) should be equal to the sum of their radii. So, if we let r_A, r_B, r_C be the radii at vertices A, B, C respectively,...

    work page

  27. [27]

    Let me try that

    r_B + r_C = 10 Solving these equations should give the values of the radii. Let me try that. Subtract equation 3 from equation 1: (r_A + r_B) - (r_B + r_C) = 20 - 10→r_A - r_C = 10. So r_A = r_C + 10. Then substitute 13 into equation 2: (r_C + 10) + r_C = 10 √ 3 →2r_C + 10 = 10 √ 3→2r_C = 10( √ 3- 1) →r_C = 5( √ 3- 1). Then r_A = 5( √ 3- 1) + 10 = 5 √ 3+ ...

    work page

  28. [28]

    Now, the next part is to find the area of the union of the circles intersected with the triangle

    So the radii are found. Now, the next part is to find the area of the union of the circles intersected with the triangle. Since the circles are externally tangent, they don’t overlap inside the triangle. Therefore, the area we want is simply the sum of the sectors of each circle that lie within the triangle. Each sector corresponds to the angle at each ve...

    work page

  29. [29]

    r_B²= (15 - 5 √ 3)² = 225 - 150 √ 3+ 75 = 300 - 150 √

    work page

  30. [30]

    Sector A: (1/12)(100 + 50 √ 3)π= 25/3π+ 25 √ 3/6π Sector B: (1/6)(300 - 150 √ 3)π= 50π- 25 √ 3π Sector C: (1/4)(100 - 50 √ 3)π= 25π- 25 √ 3/2π Addingπterms: 25/3 + 50 + 25 = 250/3π

    r_C² = (5 √ 3- 5)²= 75 - 50 √ 3+ 25 = 100 - 50 √ 3. Sector A: (1/12)(100 + 50 √ 3)π= 25/3π+ 25 √ 3/6π Sector B: (1/6)(300 - 150 √ 3)π= 50π- 25 √ 3π Sector C: (1/4)(100 - 50 √ 3)π= 25π- 25 √ 3/2π Addingπterms: 25/3 + 50 + 25 = 250/3π. Adding √ 3terms: 25 √ 3/6 - 25 √ 3 - 25 √ 3/2. Convert to sixths: 25/6 - 150/6 - 75/6 = -200/6 = -100/3. So total area is (...

    work page

  31. [31]

    Reference Anchor: The reference solution correctly considers the different types of biological interactions (catalysis, reaction, activation, and expression) and logically narrows down to the most plausible relationship, which is activation given the options

    work page

  32. [32]

    The Fault Line: The flawed solution incorrectly chose ‘Expression’ as the relationship, which is a broader term and not as specific as ‘Activation’

    work page

  33. [33]

    <critique> When determining the specific relationship between two proteins, prioritize direct functional interactions over broader regulatory effects

    Root Cause Analysis: The flawed solution missed the nuance that ‘Activation’ is a more direct regulatory interaction compared to ‘Expression’, which pertains to the regulation of gene production rather than functional interaction. <critique> When determining the specific relationship between two proteins, prioritize direct functional interactions over bro...

    work page

  34. [34]

    Step-by-step Breakdown: The solution examines the amino acid sequence for its composition and potential interactions, then uses logical deduction to identify the most plausible stability score based on the given options and the sequence’s characteristics

    work page

  35. [35]

    Verification: The reasoning aligns with the typical features of protein folding stability, where a moderate positive score indicates reasonable stability, and the sequence contains both hydrophobic and charged residues, which can contribute to a moderate stability

    work page

  36. [36]

    The reasoning was direct and grounded in the properties of the sequence

    Detour Check: No significant detours or alternative approaches were explored. The reasoning was direct and grounded in the properties of the sequence

    work page

  37. [37]

    Goldilocks effect

    Core Insight Extraction: The key insight is that the stability score of a protein sequence can be estimated based on its amino acid composition and the nature of the interactions between residues. <hint> The solution directly analyzes the amino acid sequence to estimate stability. The key insight is that a moderate positive stability score is consistent w...

    work page 2024