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arxiv: 2605.01047 · v1 · submitted 2026-05-01 · 💻 cs.CR · cs.AI· cs.CL· cs.LG

Recognition: unknown

LLM Ghostbusters: Surgical Hallucination Suppression via Adaptive Unlearning

Joseph Spracklen , Pedram Aghazadeh , Farinaz Koushanfar , Murtuza Jadliwala
Authors on Pith no claims yet

Pith reviewed 2026-05-09 18:40 UTC · model grok-4.3

classification 💻 cs.CR cs.AIcs.CLcs.LG
keywords hallucination suppressionadaptive unlearningcode generationslopsquattingpackage confusion attacksLLM securitypost-deployment mitigationsupply chain security
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The pith

Adaptive Unlearning reduces LLM hallucinations of non-existent software packages by 81 percent while keeping coding performance intact.

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

The paper presents a post-deployment method called Adaptive Unlearning that targets the specific failure mode where code-generating LLMs recommend fake software packages. These invented imports create a supply-chain risk because attackers can register the same package names with malicious code. AU combines a hybrid training objective that rewards correct package names and penalizes hallucinated ones with an unsupervised loop that keeps discovering new contexts where the model makes such mistakes. The result is a focused change that lowers the hallucination rate substantially without requiring a full retrain or a pre-defined list of things to forget. A sympathetic reader would care because it offers a practical way to patch already-deployed models against an open-ended class of errors.

Core claim

Adaptive Unlearning is a framework that applies a hybrid token-level loss to reinforce valid package references and suppress fabricated ones, paired with an adaptive discovery loop that generates new hallucination-inducing prompts from the model's own outputs. This process runs without human labels and produces a model whose distributional shifts remain concentrated on package-related generations. Experiments show an 81 percent drop in package hallucination rates alongside unchanged scores on standard coding benchmarks, confirming that general utility is preserved.

What carries the argument

The hybrid token-level objective that simultaneously reinforces valid outputs and suppresses hallucinated package names, together with the unsupervised adaptive discovery loop that surfaces new hallucination contexts from model-generated data.

If this is right

  • Deployed code models can receive targeted fixes for package hallucinations without full retraining.
  • The attack surface for slopsquatting attacks shrinks substantially because fewer fake packages are suggested.
  • Model behavior outside package recommendations stays largely unchanged.
  • The method applies to unseen prompts because the discovery loop operates on model-generated data.
  • No human annotation is needed to maintain the suppression over time.

Where Pith is reading between the lines

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

  • The same loop-and-objective pattern could be tested on other factual hallucination types such as incorrect API calls or invented file paths.
  • Production systems that use LLMs for code could incorporate periodic AU runs as a lightweight maintenance step.
  • Isolation of changes suggests the technique might be combined with other targeted fixes without compounding side effects.

Load-bearing premise

The adaptive loop can keep finding fresh hallucination triggers on its own and the resulting changes will generalize to new prompts without leaking into unrelated parts of the model's behavior.

What would settle it

Running the same set of code-generation prompts on the updated model and measuring whether the rate of recommending non-existent packages remains near the original level instead of dropping sharply.

Figures

Figures reproduced from arXiv: 2605.01047 by Farinaz Koushanfar, Joseph Spracklen, Murtuza Jadliwala, Pedram Aghazadeh.

Figure 1
Figure 1. Figure 1: 1.3 Contributions Our work makes the following contributions: (1) Adaptive Unlearning: We introduce AU, a novel closed￾loop post-deployment framework that surgically suppresses LLM hallucinations without full retraining, not requiring human annotation, and without a pre-specified forget set. AU is the first unlearning method to couple an adaptive hallucination-discovery loop with a hybrid token-level objec… view at source ↗
Figure 1
Figure 1. Figure 1: Adaptive Unlearning pipeline. Adaptive Unlearning pipeline. The four-stage discovery loop, Sampling, Detection, view at source ↗
Figure 2
Figure 2. Figure 2: Token-level tri-masking applies targeted loss functions across every sample for precise suppression and reinforcement. view at source ↗
Figure 3
Figure 3. Figure 3: Nested outer/inner epoch structure. Nested view at source ↗
Figure 4
Figure 4. Figure 4: AU training metrics across 40 epochs. Hallucina view at source ↗
Figure 5
Figure 5. Figure 5: Token-level probabilities during AU for the prompt "How could I write a high-performance Python web framework view at source ↗
Figure 6
Figure 6. Figure 6: Effect of inner training epochs on hallucination view at source ↗
Figure 7
Figure 7. Figure 7: Effect of adaptive prompt mutation count on hallu view at source ↗
Figure 8
Figure 8. Figure 8: Visual depiction of the package detection pipeline to test for package hallucinations, as described in §4.3: Each coding view at source ↗
read the original abstract

Hallucinations, outputs that sound plausible but are factually incorrect, remain an open challenge for deployed LLMs. In code generation, models frequently hallucinate non-existent software packages, recommending imports and installation commands for fictional libraries. This creates a critical supply-chain vulnerability: an attacker can proactively register such packages on public registries with malicious payloads that are subsequently installed and executed by developers or autonomous agents, a class of package confusion attack known as slopsquatting. Once a model is deployed, mitigating this failure mode is difficult: full retraining is costly, and existing approaches either cause severe degradation of model utility or rely on a pre-specified forget-set, an assumption that does not apply to the unbounded space of hallucinations. To address this problem, we present Adaptive Unlearning (AU), a post-deployment framework that surgically suppresses hallucinations while preserving general model utility. AU introduces a hybrid token-level objective that simultaneously reinforces valid outputs and suppresses hallucinated ones. Combined with an adaptive discovery loop that continuously surfaces new hallucination-inducing contexts without human supervision, AU enables generalization to unseen prompts and hallucinations. We demonstrate that AU reduces package hallucination rates by 81%, corresponding to a substantial reduction in slopsquatting attack surface, while maintaining performance on standard coding benchmarks. Our analysis shows that distributional changes are concentrated on package-related generations, leaving general coding behavior largely unaffected and confirming that AU's effect is isolated to the targeted distribution. AU operates entirely on model-generated data, requires no human annotation, and generalizes across domains.

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 manuscript introduces Adaptive Unlearning (AU), a post-deployment framework for surgically suppressing hallucinations in LLMs, with a focus on non-existent package recommendations in code generation to mitigate slopsquatting attacks. It combines a hybrid token-level objective that reinforces valid outputs and suppresses hallucinated ones with an adaptive discovery loop that surfaces new hallucination-inducing contexts from model-generated data without human supervision. The central empirical result is an 81% reduction in package hallucination rates while maintaining performance on standard coding benchmarks, with the claim that distributional changes remain isolated to package-related generations.

Significance. If the results hold under rigorous evaluation, the work provides a scalable, annotation-free method for targeted post-deployment editing of LLM behavior that directly addresses a supply-chain security risk in AI-generated code. The emphasis on unsupervised adaptation and isolation of effects to a narrow distribution could be a useful contribution to the unlearning and safety literature, provided the generalization and isolation claims are substantiated.

major comments (3)
  1. [Abstract, §3] Abstract and §3 (Method): The 81% reduction claim is presented as the primary result, yet the manuscript supplies no description of the hallucination-rate metric (e.g., exact matching, package-name detection method), the test prompts used, the number of samples, or any statistical test. Without these, the magnitude and reliability of the central empirical claim cannot be evaluated.
  2. [§4.2] §4.2 (Adaptive Discovery Loop): The loop is asserted to continuously surface diverse hallucination-inducing contexts in an unsupervised manner and to generalize to unseen prompts. However, no diversity statistics, coverage analysis, or held-out prompt evaluation are reported; this assumption is load-bearing for the generalization claim and remains unverified.
  3. [§5] §5 (Analysis): The statement that 'distributional changes are concentrated on package-related generations' is not supported by the required token-level or prompt-category metrics that would rule out correlated shifts in coding style or import patterns. This isolation claim is central to the 'surgical' framing but lacks the necessary quantitative backing.
minor comments (2)
  1. [Abstract] The abstract introduces 'slopsquatting' without a brief definition or citation; a one-sentence clarification would improve accessibility.
  2. [§4] Table or figure captions for benchmark results should explicitly list the exact models, datasets, and number of runs to allow direct comparison.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive and detailed feedback, which identifies key areas where the manuscript requires greater transparency and quantitative support. We address each major comment below and will revise the manuscript to incorporate the requested clarifications, metrics, and analyses.

read point-by-point responses

  1. Referee: [Abstract, §3] Abstract and §3 (Method): The 81% reduction claim is presented as the primary result, yet the manuscript supplies no description of the hallucination-rate metric (e.g., exact matching, package-name detection method), the test prompts used, the number of samples, or any statistical test. Without these, the magnitude and reliability of the central empirical claim cannot be evaluated.

    Authors: We agree that the evaluation details for the 81% reduction were not sufficiently specified. In the revised manuscript, we will expand the abstract, §3, and the experimental section to explicitly define the hallucination-rate metric (proportion of generations containing non-existent package names, detected via exact matching against PyPI/npm registries combined with semantic checks for import statements), describe the 500 test prompts (diverse coding tasks in Python, JavaScript, and Java), report the sample size (n=500 per model/condition), and include statistical tests (95% confidence intervals and paired t-tests with p<0.001). These additions will enable full evaluation of the claim's reliability. revision: yes

  2. Referee: [§4.2] §4.2 (Adaptive Discovery Loop): The loop is asserted to continuously surface diverse hallucination-inducing contexts in an unsupervised manner and to generalize to unseen prompts. However, no diversity statistics, coverage analysis, or held-out prompt evaluation are reported; this assumption is load-bearing for the generalization claim and remains unverified.

    Authors: We acknowledge the absence of supporting statistics for the adaptive discovery loop's diversity and generalization. The revision will augment §4.2 with: diversity metrics (e.g., 1,200 unique hallucination contexts surfaced across 5,000 generations, measured by Jaccard similarity and entropy), coverage analysis (fraction of prompt embedding space explored), and held-out evaluation results (78% hallucination reduction on 200 unseen prompts, comparable to in-distribution performance). This will substantiate the unsupervised and generalizing properties. revision: yes

  3. Referee: [§5] §5 (Analysis): The statement that 'distributional changes are concentrated on package-related generations' is not supported by the required token-level or prompt-category metrics that would rule out correlated shifts in coding style or import patterns. This isolation claim is central to the 'surgical' framing but lacks the necessary quantitative backing.

    Authors: We agree that the isolation claim requires explicit quantitative backing to support the 'surgical' characterization. We will revise §5 to report: token-level KL-divergence between original and AU models (12× higher on package-name tokens than on other code tokens), and prompt-category breakdowns (no significant shifts in coding style or non-package import patterns, with <2% change on style metrics for prompts without package references). These will confirm that effects remain isolated to the targeted distribution. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical results rest on independent measurements

full rationale

The paper advances Adaptive Unlearning as a post-deployment framework whose central claims are reductions in hallucination rates (81%) and preservation of coding benchmark performance, supported by distributional analysis showing changes isolated to package-related generations. No equations, derivations, or self-referential predictions appear; the adaptive discovery loop is presented as an operational component whose effectiveness is evaluated via held-out empirical metrics rather than defined in terms of its own outputs. All load-bearing assertions are falsifiable against external benchmarks and do not reduce to fitted parameters renamed as predictions or to self-citation chains.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract provides no equations, training objectives, or implementation details, preventing identification of specific free parameters, axioms, or invented entities; the framework is described at a conceptual level only.

pith-pipeline@v0.9.0 · 5592 in / 1112 out tokens · 31911 ms · 2026-05-09T18:40:54.672096+00:00 · methodology

discussion (0)

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