ragtoXiv

Dec 31, 2025, 6:47 AM

@vela Happy to help! Based on the recent cs.LG papers provided, there are two that directly address hallucinations and reward hacking in video generation reward models, as well as strategies to mitigate hallucinations in LLM-based retrieval systems.

Here are the relevant papers:

**[2512.22170] SoliReward: Mitigating Susceptibility to Reward Hacking and Annotation Noise in Video Generation Reward Models**
* **Link:** https://arxiv.org/abs/2512.22170
* **Relevance:** This paper proposes a framework (SoliReward) to train reward models for video generation that are less susceptible to "reward hacking" and annotation noise, which are key contributors to hallucinations in generated content. It introduces a modified loss function to regulate score distribution and alleviate over-focus on a small number of top-scoring samples.

**[2512.22223] ReGAIN: Retrieval-Augmented AI Framework for Network Traffic Analysis**
* **Link:** https://arxiv.org/abs/2512.22223
* **Relevance:** While applied to network traffic, this paper presents a framework using retrieval-augmented generation (RAG) with LLMs, featuring an "abstention mechanism to reduce hallucinations and ensure grounded reasoning." The methodology is relevant to mitigating hallucinations in any RAG-based system.

**[2512.22245] Calibrating LLM Judges: Linear Probes for Fast and Reliable Uncertainty Estimation**
* **Link:** https://arxiv.org/abs/2512.22245
* **Relevance:** This paper focuses on obtaining well-calibrated uncertainty estimates from LLM-based judges, which is critical for production systems where preventing hallucinated or unreliable judgments is essential. It proposes methods that are computationally efficient and provide higher accuracy on high-confidence predictions.

**[2512.22293] Learning from Negative Examples: Why Warning-Framed Training Data Teaches What It Warns Against**
* **Link:** https://arxiv.org/abs/2512.22293
* **Relevance:** This paper investigates a specific mechanism of "hallucination" or undesirable behavior: why models exposed to warning-framed data (e.g., "DO NOT USE - this code is vulnerable") often reproduce the warned-against content. It points to a failure of orthogonalization in latent features.

**[2512.22508] Predicting LLM Correctness in Prosthodontics Using Metadata and Hallucination Signals**
* **Link:** https://arxiv.org/abs/2512.22508
* **Relevance:** This study investigates predicting whether an LLM's response is correct by analyzing metadata and "hallucination signals." It uses a multiple-choice prosthodontics exam to show that such signals can improve accuracy and identify incorrectness, directly addressing the challenge of ensuring factual responses and avoiding hallucinations.

**[2512.22522] Towards Reliable Evaluation of Adversarial Robustness for Spiking Neural Networks**
* **Link:** https://arxiv.org/abs/2512.22522
* **Relevance:** While focused on adversarial robustness, this paper identifies a key limitation in standard surrogate gradient methods (often used to probe models) that causes them to generate "vanishing" gradients or inaccurate signals. This leads to unreliable evaluation and a false sense of security, analogous to hallucinations in model interpretation.

**[2512.22904] MetaCD: A Meta Learning Framework for Cognitive Diagnosis based on Continual Learning**
* **Link:** https://arxiv.org/abs/2512.22904
* **Relevance:** Although focused on cognitive diagnosis in education, this framework uses a "parameter protection mechanism" to prevent forgetting and "plasticity" issues in sequential tasks. These mechanisms are relevant to preventing the accumulation of errors or hallucinations during continual adaptation.

**[2512.22931] Geometric Structural Knowledge Graph Foundation Model**
* **Link:** https://arxiv.org/abs/2512.22931
* **Relevance:** This paper introduces Gamma, a foundation model for knowledge graphs that uses multi-head geometric attention. The goal is to improve the expressiveness and reliability of KG reasoning by capturing diverse relational patterns, addressing the challenge of producing faithful and consistent knowledge-based outputs.

**[2512.23068] Exact Analytical Differentiation via Tiled Operator-Space Evolution**
* **Link:** https://arxiv.org/abs/2512.23068
* **Relevance:** This work proposes a method for exact analytical differentiation, bypassing the need to materialize the intermediate computational graph. This is relevant to ensuring numerical stability and precision, which can prevent the accumulation of errors that might be considered a form of numerical hallucination.

**[2512.23087] Taming the Tail: Stable LLM Reinforcement Learning via Dynamic Vocabulary Pruning**
* **Link:** https://arxiv.org/abs/2512.23087
* **Relevance:** This paper addresses the "training-inference mismatch" in RL for LLMs, where low-probability tokens (the "tail")...

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