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ARXIV:2604.13006 · LLM ROBUSTNESS · SUBMITTED 15 APR · 17:00 UTC · FRESHNESS STALE
ARXIV:2604.13006LLM ROBUSTNESSSUBMITTED 15 APR · 17:00 UTCFRESHNESS STALEErfan Baghaei Potraghloo · Seyedarmin Azizi · Souvik Kundu · Massoud Pedram · arXiv
Reveals that instruction-tuned LLMs are fragile to simple lexical constraints, leading to significant response collapse, and identifies a planning failure as the root cause.
Opportunity summary
Pain Reveals that instruction-tuned LLMs are fragile to simple lexical constraints, leading to significant response collapse, and identifies a planning failure as the root cause.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
Reveals that instruction-tuned LLMs are fragile to simple lexical constraints, leading to significant response collapse, and identifies a planning failure as the root cause. We show that simple lexical constraints (banning a single punctuation…
Instruction-tuned large language models produce helpful, structured responses, but how robust is this helpfulness when trivially constrained? We show that simple lexical constraints (banning a single punctuation character or common word) cause instruction-tuned LLMs…
ScienceToStartup currently rates this 6.0/10 on the public viability pass. We show that simple lexical constraints (banning a single punctuation character or common word) cause instruction-tuned LLMs to collapse their responses, losing 14--48% of…
LLM Robustness moved forward this cycle; last verified April 2026. Public score 6.0/10. Production flags indicate code availability.
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Reveals that instruction-tuned LLMs are fragile to simple lexical constraints, leading to significant response collapse, and identifies a planning failure as the root cause.
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10.48550/arXiv.2604.13006Reveals that instruction-tuned LLMs are fragile to simple lexical constraints, leading to significant response collapse, and identifies a planning failure as the root cause.
Abstract
Instruction-tuned large language models produce helpful, structured responses, but how robust is this helpfulness when trivially constrained? We show that simple lexical constraints (banning a single punctuation character or common word) cause instruction-tuned LLMs to collapse their responses, losing 14--48% of comprehensiveness in pairwise evaluation across three open-weight model families and one closed-weight model (GPT-4o-mini). The baseline response is preferred in 77--100% of 1,920 pairwise comparisons judged by GPT-4o-mini and GPT-4o. Notably, GPT-4o-mini suffers 31% comprehensiveness loss (99% baseline win rate), demonstrating that the fragility extends to commercially deployed closed-weight models, contrary to prior findings on format-level constraints. Through mechanistic analysis, we identify this as a planning failure: two-pass generation (free generation followed by constrained rewriting) recovers 59--96% of response length, and linear probes on prompt representations predict response length with $R^2 = 0.51$--$0.93$ before generation begins, with $R^2$ tracking collapse severity across models. The same probes yield negative $R^2$ on base models, confirming that instruction tuning creates the representational structure encoding the collapse decision. Crucially, base models show no systematic collapse under identical constraints, with effects that are small, noisy, and bidirectional, demonstrating that instruction tuning creates this fragility by coupling task competence to narrow surface-form templates. The effect replicates on MT-Bench across all eight task categories. We further show that standard independent LLM-as-judge evaluation detects only a 3.5% average quality drop where pairwise evaluation reveals 23%, exposing a methodological blind spot in how constrained generation is assessed.
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Proof status
unverified0 refs; 3 sources; 50% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
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Dimensions overall score 6.0
PROBLEM
Reveals that instruction-tuned LLMs are fragile to simple lexical constraints, leading to significant response collapse, and identifies a planning failure as the root cause. We show that simple lexical constraints (banning a single punctuation character or common word) cause ins...
METHOD
Instruction-tuned large language models produce helpful, structured responses, but how robust is this helpfulness when trivially constrained? We show that simple lexical constraints (banning a single punctuation character or common word) cause instruction-tuned LLMs to collapse...
RESULT
ScienceToStartup currently rates this 6.0/10 on the public viability pass. We show that simple lexical constraints (banning a single punctuation character or common word) cause instruction-tuned LLMs to collapse their responses, losing 14--48% of comprehensiveness in pairwise ev...
WHY NOW
LLM Robustness moved forward this cycle; last verified April 2026. Public score 6.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
Reveals that instruction-tuned LLMs are fragile to simple lexical constraints, leading to significant response collapse, and identifies a planning failure as the root cause. We show that simple lexical constraints (banning a single punctuation character or common word) cause instruction-tuned LLMs to collapse their responses, losing 14--48% of comprehensiveness in pairwise evaluation across three open-weight model families and one closed-weight model (GPT-4o-mini).
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Instruction-tuned large language models produce helpful, structured responses, but how robust is this helpfulness when trivially constrained? We show that simple lexical constraints (banning a single punctuation character or common word) cause instruction-tuned LLMs to collapse their responses, losing 14--48% of comprehensiveness in pairwise evaluation across three open-weight model families and one closed-weight model (GPT-4o-mini).
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 6.0/10 on the public viability pass. We show that simple lexical constraints (banning a single punctuation character or common word) cause instruction-tuned LLMs to collapse their responses, losing 14--48% of comprehensiveness in pairwise evaluation across three open-weight model families and one closed-weight model (GPT-4o-mini). Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
LLM Robustness moved forward this cycle; last verified April 2026. Public score 6.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Reveals that instruction-tuned LLMs are fragile to simple lexical constraints, leading to significant response collapse, and identifies a planning failure as the root cause.
Segment
LLM Robustness
Adoption evidence
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Commercial read
6.0/10 public viability
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reason
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proof status
unverified
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next verification path
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stale
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Build readiness
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passport absent
stale
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Artifact maturity
GitHub and Hugging Face maturity payloads
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stale
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Technical feasibility
partial
Current read
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Gaps
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Buyer clarity
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missing
Current read
No public implementation surface observed.
Evidence
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Write integration checklist from prototype path and target workflow.
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DEFENSIBILITY
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