R Shah

Mayank Kharbanda, Michael Cochez, Rajiv Ratn Shah et al.

Logical Multi-Hop Query Answering over Knowledge Graphs (KGs) can be formulated as querying, with an implicit completeness assumption. Current works mainly focus on Existential First Order Logic (EFO) queries. These EFO queries contain conjunction, disjunction, and negation operators. Most existing works employ transductive reasoning, meaning they are not capable of reasoning over entities unseen during training. In the real world, there is a resource scarcity, and we cannot train a model with all the nodes of a large KG. Hence, we propose InductWave, a wavelet-based inductive embedding method for logical query answering on large KGs. Here, the training graph consists of fewer nodes than the test graph. Our model performs on par with the baseline models while having half the number of message-passing layers. It outperforms all of them in most cases, with 75% of the layers. These fewer resource requirements enable us to evaluate InductWave on massive graphs, such as Wiki-KG. We test our model using extensive experiments across varying train-test graph proportions of the FB15k-(237) dataset, comparing it with the state-of-the-art models. The code and datasets for the model are available at https://github.com/kracr/inductwave/.

Raj Jaiswal, Dhruv Jain, Rishabh Dhawan et al.

Physics reasoning fails structurally in small language models: an error at any step propagates forward, corrupting every inference that follows. Limited domain knowledge, hallucination under multi-step derivation, and distributional sensitivity compound this failure. We propose a step-level reward framework that identifies the first reasoning error, generates targeted structured feedback, and trains the model to revise its solution via policy gradient with KL regularization, without exposing it to ground truth solutions as generation targets. Unlike annotation-dependent step-level methods, no preference data construction is required and the external verifier operates exclusively at training time. Across five physics benchmarks, our framework delivers accuracy gains of 17-20% over CoT prompting and 10-16% over the strongest baseline, reduces calculation errors from 56.9% to 23.5%, and reduces miscomprehension errors from 22.3% to 12.0% in the best observed cases. Conceptual errors reduce from 89.7% to 68.7%, yet persist as the hardest failure mode across all conditions.

Sumit Kumar, Sayantan Dasgupta, Kushal Mitra et al.

State-of-the-art AI training simulators assume homogeneous compute and network infrastructure. However, real-world training infrastructure is becoming increasingly heterogeneous since: (a) Model architectures such as multimodal and MoE exploit heterogeneity to improve device utilization, (b) Public cloud platforms often provide limited availability of homogeneous hardware due to fast hardware evolution, and (c) Large enterprises frequently deploy geographically distributed infrastructure that is both diverse and heterogeneous. In this paper, we present Xsim, a heterogeneity-aware simulator for distributed LLM training. Xsim supports: (i) Load balancing through non-uniform workload partitioning across heterogeneous device groups, (ii) Heterogeneity-aware collective communication via customized ring construction and chunk partitioning, (iii) Reusable heterogeneity-aware abstractions for emerging pipeline-parallel algorithms and non-uniform tensor resharding technique, (iv) Flexible input abstractions for specifying deployment plans with custom device groups and custom device-to-parallelism mappings, and (v) Pluggable integration with NS-3 and htsim, allowing users to trade off simulation fidelity for performance and scalability. Our evaluation demonstrates that Xsim accurately predicts training time for real-world heterogeneous deployments, with an error of less than 5% across most heterogeneous data-parallel/tensor-parallel configurations and around 2% error with pipeline-parallel communication modeling. We expose actionable metrics such as pipeline bubble time and straggler waiting time.