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arxiv: 2606.26918 · v1 · pith:KCD7T6H7new · submitted 2026-06-25 · 💻 cs.AI

Diagnosing Task Insensitivity in Language Agents

Jingyu Liu , Xiaopeng Wu , Kehan Chen , Chuan Yu , Yong Liu This is my paper

Pith reviewed 2026-06-26 04:56 UTC · model grok-4.3

classification 💻 cs.AI
keywords task insensitivitylanguage agentsout-of-distribution generalizationattention driftcontrastive regularizationlarge language modelsagent behavior
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The pith

Language agents keep repeating training actions even when task instructions are changed or corrupted.

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

The paper identifies task insensitivity as a main reason language-model agents generalize poorly outside their training distribution. When similar but different tasks appear, or when the original instruction is altered so it cannot be answered, the models still produce the same actions they learned in training. This pattern lines up with attention moving away from the task description and toward immediate observations while the model trains. The authors introduce Task-Perturbed NLL Optimization, a contrastive regularizer that forces the model to make its actions depend on the task text. Experiments show the change raises sensitivity to the current task and lifts performance on new tasks while keeping attention on the instructions.

Core claim

Models exhibit task insensitivity by continuing actions aligned with the original task even when the instruction is semantically corrupted and cannot be directly answered, and by outputting the same action when the task description is replaced with a similar but distinct one. This is accompanied by consistent training-time attention drift away from task tokens toward local observations. Task-Perturbed NLL Optimization, a lightweight contrastive regularizer, explicitly encourages action dependence on the task instruction and improves task sensitivity and OOD generalization while preserving more stable attention to task tokens.

What carries the argument

Task-Perturbed NLL Optimization, a contrastive regularizer added to training that perturbs the task description to force the model's actions to depend on the current instruction rather than on learned shortcuts.

If this is right

  • Agents will change their actions when the task instruction changes, rather than defaulting to training patterns.
  • Attention weights will remain higher on task tokens throughout training and inference.
  • Out-of-distribution performance on related but non-identical tasks will rise while in-distribution performance stays stable.
  • Shortcut learning that ignores instruction semantics will decrease in long-horizon agent settings.

Where Pith is reading between the lines

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

  • The same attention-drift pattern may appear in other instruction-following domains such as code generation or tool use when prompts are varied.
  • Tracking attention on instruction tokens during training could provide an early diagnostic for generalization risk before deployment.
  • Applying similar perturbations to other prompt sections, such as history or observations, might further reduce reliance on local cues.

Load-bearing premise

The observed attention drift is the direct cause of task insensitivity, and adding the contrastive regularizer will increase dependence on task tokens without creating new failure modes.

What would settle it

A controlled test in which the regularizer is applied but task sensitivity on held-out similar tasks does not increase or attention on task tokens decreases further.

Figures

Figures reproduced from arXiv: 2606.26918 by Chuan Yu, Jingyu Liu, Kehan Chen, Xiaopeng Wu, Yong Liu.

Figure 1
Figure 1. Figure 1: The model might overfit to the trained tasks. [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Examples of corrupted task descriptions and [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Probability that models appear to reconstruct the original task intent from a corrupted task description. [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Probability that the predicted action under a corrupted task description remains aligned with the action [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Similarity between the task inferred from a corrupted description and the corresponding original task, [PITH_FULL_IMAGE:figures/full_fig_p004_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Probability that the model generates the same action after the task description is replaced with another [PITH_FULL_IMAGE:figures/full_fig_p005_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Attention allocated to task description across training checkpoints during action generation. Across model [PITH_FULL_IMAGE:figures/full_fig_p006_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Model performance on OOD tasks. The results show that our method improves OOD performance in the [PITH_FULL_IMAGE:figures/full_fig_p008_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Attention allocated to the task-instruction [PITH_FULL_IMAGE:figures/full_fig_p008_9.png] view at source ↗
Figure 11
Figure 11. Figure 11: SFT model performance on in-domain tasks. The results show that our method improves in-domain [PITH_FULL_IMAGE:figures/full_fig_p014_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Attention allocated to available actions regions across training checkpoints during action generation. [PITH_FULL_IMAGE:figures/full_fig_p016_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Attention allocated to current observation regions across training checkpoints during action generation. [PITH_FULL_IMAGE:figures/full_fig_p016_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Attention allocated to other prompt regions across training checkpoints during action generation. Across [PITH_FULL_IMAGE:figures/full_fig_p016_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Attention allocated to response style regions across training checkpoints during action generation. Across [PITH_FULL_IMAGE:figures/full_fig_p017_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: Attention allocated to task regions across training checkpoints of GRPO during action generation. Across [PITH_FULL_IMAGE:figures/full_fig_p017_16.png] view at source ↗
read the original abstract

Large language models can serve as capable long-horizon agents, but their out-of-distribution (OOD) generalization remains weak. We identify a key source of this failure as task insensitivity: when faced with similar but distinct tasks, models might apply patterns learned during training and fail to solve the task at hand. We show that models often continue with actions aligned with the original task even when the instruction is semantically corrupted and cannot be directly answered. We further find that, when we replace the task description in a trained prompt with another similar but distinct task, the model may still output the same action. This behavior is accompanied by a consistent training-time attention drift away from task tokens and toward local observations, suggesting an optimization bias toward shortcuts. To mitigate this problem, we propose Task-Perturbed NLL Optimization, a lightweight contrastive regularizer that explicitly encourages action dependence on the task instruction. Extensive evaluations show that our intervention improves task sensitivity and OOD generalization while preserving more stable attention to task tokens.

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 / 1 minor

Summary. The paper claims that task insensitivity—persistent execution of training-task actions despite semantically corrupted or swapped instructions—is a key driver of weak OOD generalization in LLM agents. It reports that this behavior co-occurs with training-time attention drift away from task tokens toward local observations, interprets the drift as evidence of an optimization bias toward shortcuts, and introduces Task-Perturbed NLL Optimization (a contrastive regularizer) to enforce action dependence on the task description. The abstract states that extensive evaluations demonstrate improved task sensitivity, OOD generalization, and more stable task-token attention.

Significance. If the quantitative results and controls hold, the work supplies a concrete diagnostic lens and a lightweight mitigation for shortcut learning in long-horizon agents, an issue that limits deployment reliability. The explicit linkage of attention patterns to behavioral failure modes and the proposal of a contrastive regularizer that can be added to existing NLL training are potentially useful contributions.

major comments (3)
  1. [Abstract] Abstract: the central diagnosis treats the observed attention drift as evidence of a causal optimization bias toward shortcuts, yet the text only describes the drift as 'consistent' and 'suggesting' bias. No intervention (e.g., attention masking or forced re-weighting of task tokens) is reported that would test whether preventing the drift reduces task insensitivity, leaving the causal claim unsupported.
  2. [Experiments section] Experiments / evaluation section: the claim that Task-Perturbed NLL Optimization improves OOD generalization rests on the assumption that explicitly increasing task-token dependence fixes the root cause without new failure modes. No ablations are described that compare against other regularizers, measure side effects on in-distribution performance, or test whether the regularizer merely masks symptoms rather than addressing the underlying insensitivity.
  3. [Abstract] Abstract and method description: the paper asserts 'extensive evaluations' but supplies no quantitative metrics, dataset sizes, baseline comparisons, error bars, or statistical tests in the provided summary. Without these details it is impossible to assess whether the reported improvements in task sensitivity are robust or merely consistent with the untested causality assumption.
minor comments (1)
  1. [Method] Notation for the contrastive regularizer (Task-Perturbed NLL) should be defined with an explicit loss equation rather than described only in prose.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive comments. We address each major point below with honest responses based on the current manuscript and indicate planned revisions where the concerns are valid.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central diagnosis treats the observed attention drift as evidence of a causal optimization bias toward shortcuts, yet the text only describes the drift as 'consistent' and 'suggesting' bias. No intervention (e.g., attention masking or forced re-weighting of task tokens) is reported that would test whether preventing the drift reduces task insensitivity, leaving the causal claim unsupported.

    Authors: The manuscript uses 'suggesting' and 'consistent with' precisely to avoid claiming direct causality; the drift is reported as an observed co-occurrence with task insensitivity. No attention-level intervention (such as masking) was performed, as the work centers on behavioral diagnosis and a training-time regularizer rather than mechanistic interventions on attention. We agree this leaves the causal interpretation open and will revise the abstract and discussion to explicitly label the link as correlational, adding a limitations paragraph noting the absence of such interventions. revision: partial

  2. Referee: [Experiments section] Experiments / evaluation section: the claim that Task-Perturbed NLL Optimization improves OOD generalization rests on the assumption that explicitly increasing task-token dependence fixes the root cause without new failure modes. No ablations are described that compare against other regularizers, measure side effects on in-distribution performance, or test whether the regularizer merely masks symptoms rather than addressing the underlying insensitivity.

    Authors: The current experiments compare the proposed regularizer only against standard NLL training and do not include ablations against alternative regularizers, in-distribution side-effect measurements, or explicit tests for symptom masking versus root-cause mitigation. These omissions constitute a genuine gap. We will add the requested ablations in revision, including comparisons to other contrastive losses, in-distribution performance tables, and controls that vary task perturbation strength. revision: yes

  3. Referee: [Abstract] Abstract and method description: the paper asserts 'extensive evaluations' but supplies no quantitative metrics, dataset sizes, baseline comparisons, error bars, or statistical tests in the provided summary. Without these details it is impossible to assess whether the reported improvements in task sensitivity are robust or merely consistent with the untested causality assumption.

    Authors: The abstract is intentionally high-level; the full manuscript contains the quantitative results, dataset sizes, baselines, error bars, and statistical tests in the experiments section. To address the concern, we will expand the abstract with key numerical results (e.g., task-sensitivity deltas and OOD gains) while keeping it concise. revision: yes

Circularity Check

0 steps flagged

No circularity; empirical observations and regularizer proposal are independent of inputs

full rationale

The paper's core argument rests on empirical demonstrations of task insensitivity (persistent actions under corrupted/swapped instructions) and observed attention drift during training, followed by introduction of a contrastive regularizer (Task-Perturbed NLL Optimization) motivated by those observations and supported by reported evaluations. No equations, fitted parameters renamed as predictions, self-definitional constructs, or load-bearing self-citations appear in the abstract or described content. The derivation chain does not reduce any claim to its own inputs by construction; the diagnosis and mitigation are presented as data-driven rather than tautological.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract only; no free parameters, axioms, or invented entities are specified.

pith-pipeline@v0.9.1-grok · 5703 in / 1143 out tokens · 25783 ms · 2026-06-26T04:56:02.300725+00:00 · methodology

discussion (0)

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Reference graph

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