robustness

SPAR is a pretraining objective that uses a gated KL loss to stabilize language model predictions against irrelevant prefix text, improving robustness in long-context scenarios as demonstrated on multiple benchmarks.

This paper introduces CAVEAT, a benchmark for evaluating computer-use agents in incentive-misaligned environments, revealing that agents often fail to preserve user objectives and proposes interventions to improve robustness.

This paper introduces Hallucination-R1, a robustness-oriented paraphrase generation framework designed to identify and improve factual consistency in large language models by creating semantically equivalent yet challenging paraphrases.

MME-Safety is a rigorously verified benchmark for evaluating the safety of Multimodal Large Language Models, featuring a four-dimensional annotation schema and a hierarchical framework to assess risk scenarios, harm severity, and modality-specific stealth levels.

This paper introduces the Generic Multi-Agent Trading System (GMATS) framework to study adversarial attacks on LLM-based trading systems, showing that even simple input attacks can degrade performance but robust system designs can improve robustness.

The paper introduces Sharp Mode Connectivity (SMC) to enhance the robustness of weight-space interpolation in neural networks by optimizing for flatness along entire paths, achieving significant accuracy improvements under distribution shifts.

This paper proposes a training-adaptive convolutional sparse coding framework that leverages information bottleneck principles for robust visual representation, achieving improved performance on CIFAR and ImageNet under input perturbations.

RoleBreak is an open benchmark for evaluating long-horizon role-playing robustness in spoken dialogue systems, revealing gaps in current models' ability to maintain role consistency and vocal emotion over extended interactions.

This paper studies how reinforcement learning can lead LLM agents to learn spurious tool-use policies based on superficial cues rather than task requirements, and introduces a dense reward method to mitigate this issue.

This paper demonstrates that ordinary typos in user inputs can significantly reduce the effectiveness of activation probes designed to detect prompt injection in LLMs, but introduces a KV-cache fork method that recovers most of the performance loss.

This paper proposes Fed-Equilibrium, a federated learning framework that balances robustness and fairness in clinical networks using topological Pareto control to ensure minority nodes achieve convergence comparable to dominant hubs.

DART proposes a distributional adversarial training framework for recurrent reasoning models, improving their robustness and performance on structured problems by using a local target distribution instead of single-point supervision.

This paper evaluates the robustness of LLM-generated SystemVerilog assertions under semantics-preserving RTL transformations, finding that point accuracy can hide substantial instability and advocating for robustness-aware evaluation in AI-assisted hardware verification.

The paper proposes RMS-RSP, a perturbation-sensitive method for selecting medical questions to receive rationale supervision, improving robust accuracy and semantic consistency in QA systems.

The article evaluates the robustness of PII detection systems under distribution shifts, identifies failure modes in different architectures, and proposes a hybrid detection pipeline with a QA-driven feedback loop for improved privacy protection.

This paper investigates the structural sensitivity of multilingual large language models to semantics-preserving perturbations in Hindi and Malayalam, showing significant degradation in mathematical reasoning performance and introducing the IndicReStruct benchmark for evaluation.

This paper conducts a multi-level analysis of how input perturbations propagate through decoder-only language models, assessing robustness via output behavior, hidden-state geometry, and attention-head function across models like GPT-2 and Qwen2.5.

Topological Steering is a new framework for controlling large language model behavior using topological data analysis to capture global structures in activation spaces, enabling more robust behavioral control.

This research investigates whether near-tied rankings of large language models remain robust to changes in benchmark item composition using psychometric methods, finding that while overall rankings are stable, individual close orderings can reverse based on item selection.

RL-FAT is a reinforcement learning framework for fair adversarial training that improves robustness while reducing class-wise disparities. Experiments demonstrate competitive accuracy and better fairness compared to standard methods.