arxiv: 2605.06679 · v1 · submitted 2026-04-22 · 💻 cs.LG

Recognition: no theorem link

Breaking the Illusion: When Positive Meets Negative in Multimodal Decoding

Yubo Jiang , Yitong An , Xin Yang , Abudukelimu Wuerkaixi , Xuxin Cheng , Fengying Xie , Zhiguo Jiang , Cao Liu

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Ke Zeng HaoPeng Zhang

Authors on Pith no claims yet

Pith reviewed 2026-05-11 01:22 UTC · model grok-4.3

classification 💻 cs.LG

keywords vision-language modelsobject hallucinationdecoding methodstraining-free inferencemultimodal generationvisual groundingattention imbalance

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The pith

Positive-and-Negative Decoding steers vision-language models toward visual evidence by contrasting amplified and counterfactual paths during inference.

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

Vision-language models often generate objects that are not in the image because they favor language patterns over visual input. The paper identifies an imbalance in how these models attend to visual versus linguistic information. Positive-and-Negative Decoding (PND) addresses this by running two paths in parallel: one that strengthens visual features and another that builds opposing scenarios to discourage language-only outputs. By comparing these paths at each decoding step, the method guides the model to produce more accurate, grounded descriptions. This approach works on existing models without any retraining and shows strong results on standard tests for hallucination.

Core claim

The central discovery is that object hallucination in VLMs arises from under-weighting of visual features in attention mechanisms, and that a dual-path decoding strategy can correct this. Specifically, the positive path amplifies visual evidence while the negative path constructs counterfactuals to penalize generations driven by linguistic priors. Contrasting the logits or probabilities from these two paths during autoregressive decoding enforces visual fidelity, leading to improved performance on hallucination benchmarks without requiring model fine-tuning or additional data.

What carries the argument

Positive-and-Negative Decoding (PND), a training-free dual-path contrast that amplifies visual evidence in one path and penalizes prior-dominant generation in the other.

Load-bearing premise

The attention imbalance favoring linguistic priors is the primary driver of hallucination, and the dual-path contrast will reliably enforce visual fidelity without introducing new errors or biases.

What would settle it

Applying PND to a VLM and measuring equal or higher hallucination rates on POPE or CHAIR compared to standard decoding would disprove the method's effectiveness.

Figures

Figures reproduced from arXiv: 2605.06679 by Abudukelimu Wuerkaixi, Cao Liu, Fengying Xie, HaoPeng Zhang, Ke Zeng, Xin Yang, Xuxin Cheng, Yitong An, Yubo Jiang, Zhiguo Jiang.

**Figure 1.** Figure 1: PND suppresses object hallucination via dual-path contrast. (a) A standard VLM fails to identify the frisbee due to weak linguistic priors. (b) Existing negative-only methods are insufficient. (c) Our PND’s dual pathways overcome this: the positive path reinforces the object’s presence, while the negative path creates a counterfactual, leading to correct identification. resolves this imbalance. To truly… view at source ↗

**Figure 2.** Figure 2: Overview of the PND framework for belief-adjusted decoding. Given an input image, we first extract multi-layer cross-modal attention maps to estimate query-aligned visual evidence. These maps guide the construction of two perturbed visual representations: a positive view Vpos that amplifies evidence, and a negative view Vneg that suppresses it. Passing each view through the VLM yields three logits (origina… view at source ↗

**Figure 3.** Figure 3: Empirical evidence for Bayesian imbalance in multimodal decoding. We plot the layer-wise allocation of cross-modal attention in a representative VLM. Early layers attend to visual evidence, but deeper layers shift attentional most entirely toward textual context, reflecting the accumulation of a strong language prior. This progressive decline in visual contribution indicates that p(xv | y) is underweighte… view at source ↗

**Figure 4.** Figure 4: Performance comparison of different hallucination miti [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗

**Figure 5.** Figure 5: Qualitative comparison for ”describe the scene captured in the image”. The baseline model ([regular]) produces a description [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗

read the original abstract

Vision-Language Models (VLMs) are frequently undermined by object hallucination, generating content that contradicts visual reality, due to an over-reliance on linguistic priors. We introduce Positive-and-Negative Decoding (PND), a training-free inference framework that intervenes directly in the decoding process to enforce visual fidelity. PND is motivated by our finding of an attention imbalance in VLMs, where visual features are under-weighted. Our framework introduces a dual-path contrast: a positive path that amplifies visual evidence and a negative path that constructs counterfactuals to penalize prior-dominant generation. By contrasting outputs from both paths during decoding, PND steers generation toward visually grounded results. Experiments on POPE, MME, and CHAIR demonstrate state-of-the-art performance without retraining.

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.

Desk Editor's Note private letter to a colleague

PND gives a training-free dual-path contrast for cutting object hallucinations in VLMs, but the abstract leaves the causal claims and actual gains unverified.

read the letter

The new piece is the Positive-and-Negative Decoding setup. It starts from an observed attention imbalance where visual tokens get under-weighted, then runs a positive path that boosts visual evidence and a negative path that builds counterfactuals to push back against language-prior tokens. The decoder contrasts the two at each step. That construction is more explicit than earlier contrastive decoding tricks and stays inference-only, so it could be dropped onto existing models without retraining costs. The motivation from attention patterns is clear and the target problem matters for any downstream VLM use. The abstract claims SOTA numbers on POPE, MME, and CHAIR, which would be useful if the gains are real and the baselines are standard. The stress-test concern is fair: nothing in the visible material isolates whether the attention imbalance is the main driver or just a side effect, and there is no sign of an ablation that swaps the negative path for a neutral or random one to check if the contrast itself is doing the work. Without those controls or the actual metric tables, it is impossible to tell whether the method suppresses hallucinations cleanly or simply trades one bias for another. The paper is aimed at researchers who need quick reliability fixes for vision-language generation. It is coherent on its own terms and engages the hallucination literature directly, so it clears the bar for a serious referee even though the current write-up would need the full experiments and ablations before anyone could rely on the claims.

Referee Report

2 major / 0 minor

Summary. The paper proposes Positive-and-Negative Decoding (PND), a training-free inference framework for Vision-Language Models to reduce object hallucination caused by over-reliance on linguistic priors and under-weighting of visual features in attention. PND uses a dual-path approach during decoding: a positive path that amplifies visual evidence and a negative path that constructs counterfactuals to penalize prior-dominant generation, steering outputs toward visually grounded results. It reports state-of-the-art performance on the POPE, MME, and CHAIR benchmarks without any retraining.

Significance. If the empirical claims hold, PND would represent a significant advance as a simple, training-free method to mitigate a common failure mode in VLMs. The inference-time intervention could be widely applicable and the identification of attention imbalance provides a new perspective on hallucination causes.

major comments (2)

[Abstract] Abstract: The assertion of 'state-of-the-art performance' on POPE, MME, and CHAIR is presented without any quantitative metrics, baseline comparisons, ablation studies, or statistical details. This absence prevents verification or replication of the central claim.
[Abstract] Abstract: The framework assumes that the identified attention imbalance is the primary driver of hallucination and that contrasting the negative-path counterfactuals will selectively enforce visual fidelity without introducing new errors or biases, but no derivation, isolation experiment, or control (e.g., neutral baseline replacing the negative path) is supplied to support this.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address the two major comments point by point below, clarifying the content of the full paper and committing to revisions that strengthen the abstract without altering our core claims or results.

read point-by-point responses

Referee: [Abstract] Abstract: The assertion of 'state-of-the-art performance' on POPE, MME, and CHAIR is presented without any quantitative metrics, baseline comparisons, ablation studies, or statistical details. This absence prevents verification or replication of the central claim.

Authors: We agree that the abstract would be more informative with explicit metrics. The full manuscript reports detailed quantitative results in Section 4 and Tables 1-3, including direct comparisons to prior methods on POPE (accuracy and F1), MME (perception and cognition scores), and CHAIR (hallucination rate), along with ablation studies on the dual-path components. We will revise the abstract to include the key performance deltas and main baseline references while keeping it concise. revision: yes
Referee: [Abstract] Abstract: The framework assumes that the identified attention imbalance is the primary driver of hallucination and that contrasting the negative-path counterfactuals will selectively enforce visual fidelity without introducing new errors or biases, but no derivation, isolation experiment, or control (e.g., neutral baseline replacing the negative path) is supplied to support this.

Authors: Section 3.1 of the manuscript presents attention visualizations and quantitative analysis confirming the visual-linguistic imbalance as a key factor in hallucination. The experimental section (4.3) includes ablations that isolate the negative path's contribution by comparing full PND against positive-path-only and neutral (no-contrast) variants; these controls demonstrate that the dual-path contrast improves visual grounding without adding new biases or errors, as measured by consistent gains across all benchmarks. We will add a brief reference to these controls in the abstract and expand the description of the neutral baseline if needed for clarity. revision: yes

Circularity Check

0 steps flagged

No circularity in derivation chain

full rationale

The paper frames PND as a training-free inference-time intervention motivated by an observed attention imbalance, using dual-path contrast (positive amplification of visual evidence and negative counterfactual penalization) to steer decoding. No equations, fitted parameters, or results are presented that reduce by construction to inputs, self-definitions, or self-citation chains. The central mechanism is described as an empirical contrast during decoding, with performance claims resting on external benchmark experiments (POPE, MME, CHAIR) rather than any internal loop or renamed known result. The derivation remains self-contained as a practical intervention without load-bearing reductions to prior assumptions or author-specific uniqueness theorems.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 2 invented entities

The central claim rests on the domain assumption of attention imbalance as the cause of hallucination and on the introduction of the PND dual-path mechanism as an effective corrective intervention.

axioms (1)

domain assumption VLMs exhibit an attention imbalance in which visual features are under-weighted relative to linguistic priors.
This observation is stated as the motivation for developing PND.

invented entities (2)

Positive path no independent evidence
purpose: Amplifies visual evidence during decoding
Constructed as one half of the dual-path contrast to boost image-grounded generation.
Negative path no independent evidence
purpose: Constructs counterfactuals to penalize prior-dominant generation
Invented to create contrast that discourages language-only outputs.

pith-pipeline@v0.9.0 · 5458 in / 1322 out tokens · 55843 ms · 2026-05-11T01:22:56.919798+00:00 · methodology

discussion (0)

Reference graph

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