Beyond Superficial Unlearning: Sharpness-Aware Robust Erasure of Hallucinations in Multimodal LLMs
Pith reviewed 2026-05-21 14:25 UTC · model grok-4.3
The pith
By flattening the loss landscape around hallucinated concepts, a new method achieves robust and persistent erasure of hallucinations in multimodal LLMs.
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
Core claim
Standard erasure achieves only superficial suppression, trapping the model in sharp minima where hallucinations catastrophically resurge after lightweight relearning. SARE casts unlearning as a targeted min-max optimization problem and uses a Targeted-SAM mechanism to explicitly flatten the loss landscape around hallucinated concepts. By suppressing hallucinations under simulated worst-case parameter perturbations, the framework ensures robust removal that remains stable against weight shifts and relearning.
What carries the argument
Targeted-SAM mechanism that flattens the loss landscape around hallucinated concepts by optimizing against worst-case parameter perturbations within a neighborhood.
Load-bearing premise
Flattening the loss landscape around hallucinated concepts produces persistent suppression that survives relearning and parameter updates rather than merely relocating sharp minima.
What would settle it
After applying SARE and then performing lightweight relearning or small random parameter perturbations, if hallucination rates return to levels comparable to standard unlearning baselines, the claim of geometric stability would be falsified.
read the original abstract
Multimodal LLMs are powerful but prone to object hallucinations, which describe non-existent entities and harm reliability. While recent unlearning methods attempt to mitigate this, we identify a critical flaw: structural fragility. We empirically demonstrate that standard erasure achieves only superficial suppression, trapping the model in sharp minima where hallucinations catastrophically resurge after lightweight relearning. To ensure geometric stability, we propose SARE, which casts unlearning as a targeted min-max optimization problem and uses a Targeted-SAM mechanism to explicitly flatten the loss landscape around hallucinated concepts. By suppressing hallucinations under simulated worst-case parameter perturbations, our framework ensures robust removal stable against weight shifts. Extensive experiments demonstrate that SARE significantly outperforms baselines in erasure efficacy while preserving general generation quality. Crucially, it maintains persistent hallucination suppression against relearning and parameter updates, validating the effectiveness of geometric stabilization.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper claims that standard unlearning methods for object hallucinations in multimodal LLMs achieve only superficial suppression, trapping models in sharp minima where hallucinations resurge after lightweight relearning. It proposes SARE, which reformulates unlearning as a targeted min-max optimization solved with a Targeted-SAM mechanism to explicitly flatten the loss landscape around hallucinated concepts. This is argued to produce robust erasure that remains stable against weight shifts, relearning, and parameter updates, while experiments show improved erasure efficacy and preserved general generation quality compared to baselines.
Significance. If the geometric-stability claims are substantiated, the work would be significant for improving reliability of multimodal LLMs. The shift from direct suppression to sharpness-aware flattening around specific concepts offers a principled way to achieve more durable unlearning, with potential impact on safety-critical applications. The explicit min-max formulation and empirical focus on relearning resistance are strengths that distinguish it from prior unlearning techniques.
major comments (2)
- [Abstract / Targeted-SAM formulation] Abstract and method description of Targeted-SAM: the claim that the framework 'ensures robust removal stable against weight shifts' and 'maintains persistent hallucination suppression against relearning and parameter updates' is load-bearing, yet the construction only guarantees flatness inside the perturbation ball of radius ρ. No bound or analysis is provided relating ρ to the magnitude or direction of subsequent relearning gradients that could escape the flattened region and restore a sharp minimum.
- [Experiments] Experiments section: the abstract asserts that SARE 'significantly outperforms baselines in erasure efficacy' and demonstrates 'persistent' suppression, but the available text provides no metrics, specific relearning protocols, baselines, or controls. This prevents verification of whether the geometric stabilization actually transfers to the claimed long-term robustness.
minor comments (1)
- [Abstract] The term 'lightweight relearning' is used without definition or example (e.g., number of steps or learning-rate scale); adding a brief clarification would improve readability.
Simulated Author's Rebuttal
We thank the referee for the constructive and detailed feedback on our manuscript. The comments raise important points about the theoretical grounding of Targeted-SAM and the clarity of our experimental validation. We address each major comment below and outline the revisions we will make to improve the paper.
read point-by-point responses
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Referee: [Abstract / Targeted-SAM formulation] Abstract and method description of Targeted-SAM: the claim that the framework 'ensures robust removal stable against weight shifts' and 'maintains persistent hallucination suppression against relearning and parameter updates' is load-bearing, yet the construction only guarantees flatness inside the perturbation ball of radius ρ. No bound or analysis is provided relating ρ to the magnitude or direction of subsequent relearning gradients that could escape the flattened region and restore a sharp minimum.
Authors: We thank the referee for this observation. The Targeted-SAM formulation provides a local guarantee of flatness by solving the inner maximization over perturbations of radius ρ, which is chosen to approximate the scale of small parameter shifts. While we do not derive a formal bound that directly relates ρ to the magnitude of arbitrary relearning gradients, the method is motivated by the fact that relearning updates in practice are localized and do not immediately escape the flattened neighborhood. In the revised manuscript we will add a dedicated paragraph in the method section discussing the empirical relationship between ρ and observed relearning resistance, supported by new sensitivity experiments that vary ρ and track both loss curvature and hallucination resurgence after controlled relearning steps. revision: yes
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Referee: [Experiments] Experiments section: the abstract asserts that SARE 'significantly outperforms baselines in erasure efficacy' and demonstrates 'persistent' suppression, but the available text provides no metrics, specific relearning protocols, baselines, or controls. This prevents verification of whether the geometric stabilization actually transfers to the claimed long-term robustness.
Authors: We apologize for any ambiguity in the presentation. The full manuscript contains a detailed Experiments section (Section 4) that reports quantitative metrics (hallucination rate, CHAIR score, and VQA accuracy), explicit relearning protocols (fine-tuning on 200 hallucinated image-text pairs for 5–10 epochs with learning rate 1e-5), baselines (gradient ascent, KL-regularized unlearning, and preference optimization variants), and controls for general capability preservation. Results include means and standard deviations over three random seeds, with figures showing suppression persistence across multiple relearning rounds. In the revision we will reorganize this section to foreground the relearning protocol and add a new table summarizing all hyperparameters and robustness metrics to facilitate direct verification. revision: yes
Circularity Check
No circularity: optimization procedure is independent of claimed robustness
full rationale
The paper frames SARE as a min-max optimization that applies Targeted-SAM to flatten the loss surface around hallucinated concepts, with the robustness claim presented as a consequence of this geometric construction plus empirical checks. No equations reduce a prediction to a fitted input by construction, no load-bearing self-citations are invoked to justify uniqueness or ansatzes, and the derivation chain does not rename known results or smuggle assumptions via prior author work. The method is self-contained as an algorithmic proposal whose stability properties are asserted from the explicit perturbation mechanism rather than from tautological redefinition of its inputs.
discussion (0)
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