Retrieval-augmented generation (RAG) has proven to be effective in mitigating hallucinations in large language models, yet its effectiveness remains limited in complex, multi-step reasoning scenarios. Recent efforts have incorporated search-based interactions into RAG, enabling iterative reasoning with real-time retrieval. Most approaches rely on outcome-based supervision, offering no explicit guidance for intermediate steps. This often leads to reward hacking and degraded response quality. We propose Bi-RAR, a novel retrieval-augmented reasoning framework that evaluates each intermediate step jointly in both forward and backward directions. To assess the information completeness of each step, we introduce a bidirectional information distance grounded in Kolmogorov complexity, approximated via language model generation probabilities. This quantification measures both how far the current reasoning is from the answer and how well it addresses the question. To optimize reasoning under these bidirectional signals, we adopt a multi-objective reinforcement learning framework with a cascading reward structure that emphasizes early trajectory alignment. Empirical results on seven question answering benchmarks demonstrate that Bi-RAR surpasses previous methods and enables efficient interaction and reasoning with the search engine during training and inference.

As graph tasks become pervasive in real-time and safetycritical domains (e.g., financial fraud detection and electrical power systems), it is also essential to guarantee their reliable execution beyond pursuing extraordinary performance. However, due to the neglect of consideration for graph-specific execution paradigm, existing Fault Injection (FI) reliability analysis methods typically incur inaccurate system error resilience characterization, making it challenging to provide helpful guidance for efficient and reliable graph processing paradigm design. This paper proposes GraphFI, an efficient Graph Fault Injection framework on the universal parallel tasks acceleration platform (i.e., GPGPUs). Our key insight is progressively excavating the graph-specific error propagation and effect mechanisms, thereby avoiding blind FI trials. Firstly, observing that iterations with similar active vertex set exhibit similar error behavior, we propose iteration-driven GraphFI (ID-GraphFI) to solely select representative iterations for fast error resilience profile assessment. Secondly, by detecting resilience-similarity communities in graph topology, we propose topology-driven GraphFI (TD-GraphFI) that only selects representative vertices for community overall reliability evaluation. Thirdly, by exploring the graph-specific fault monotonic property, we propose the monotonicity-driven GraphFI (MD-GraphFI) to granularly draw system severe error boundaries for predictable/unnecessary fault injection avoidance. Merging them all, GraphFI can reduce system fault site space by up to two orders of magnitude, which achieves $2.1 \sim 15.2 \times$ speedup compared to SOTA methods while providing better reliability assessment accuracy.

Abductive learning models (ABL) and neural-symbolic predictive models (NeSy) have been recently shown effective, as they allow us to infer labels that are consistent with some prior knowledge by reasoning over high-level concepts extracted from sub-symbolic inputs. However, their generalization ability is affected by reasoning shortcuts: high accuracy on given targets but leveraging intermediate concepts with unintended semantics. Although there have been techniques to alleviate reasoning shortcuts, theoretical efforts on this issue remain to be limited. This paper proposes a simple and effective analysis to quantify harm caused by it and how can mitigate it. We quantify three main factors in how NeSy algorithms are affected by reasoning shortcuts: the complexity of the knowledge base, the sample size, and the hypothesis space. In addition, we demonstrate that ABL can reduce shortcut risk by selecting specific distance functions in consistency optimization, thereby demonstrating its potential and approach to solving shortcut problems. Empirical studies demonstrate the rationality of the analysis. Moreover, the proposal is suitable for many ABL and NeSy algorithms and can be easily extended to handle other cases of reasoning shortcuts.

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