University of Connecticut

This work systematically studies how test-time compute affects predictions of pretrained tabular foundation models along three axes—adaptation, aggregation, and context construction: it introduces DiagScale, a diagonal query-key similarity update training only 0.003-0.03% of parameters that matches full fine-tuning gains across three independently pretrained backbones; on a broader pool of 96 configurations, greedy selection reduces error by 2.4% relative to the default predictor while uniform averaging increases error; attention-guided retrieval improves TabPFN-3 on some large tables, whereas the context expansion methods tested yield no consistent improvement.

This work systematically studies how test-time compute affects predictions of pretrained tabular foundation models along three axes—adaptation, aggregation, and context construction: it introduces DiagScale, a diagonal query-key similarity update training only 0.003-0.03% of parameters that matches full fine-tuning gains across three independently pretrained backbones; on a broader pool of 96 configurations, greedy selection reduces error by 2.4% relative to the default predictor while uniform averaging increases error; attention-guided retrieval improves TabPFN-3 on some large tables, whereas the context expansion methods tested yield no consistent improvement.

The work introduces BHyGNN+, a self-supervised framework for heterophilic hypergraph representation learning that contrasts augmented views of a hypergraph against its dual (with node and hyperedge roles interchanged) using cosine similarity, learning representations without ground-truth labels and without negative samples, and achieving node-classification accuracy above supervised and self-supervised baselines on eleven benchmark datasets covering heterophilic and homophilic hypergraphs plus a synthetic heterophilic dataset.

The work introduces BHyGNN+, a self-supervised framework for heterophilic hypergraph representation learning that contrasts augmented views of a hypergraph against its dual (with node and hyperedge roles interchanged) using cosine similarity, learning representations without ground-truth labels and without negative samples, and achieving node-classification accuracy above supervised and self-supervised baselines on eleven benchmark datasets covering heterophilic and homophilic hypergraphs plus a synthetic heterophilic dataset.

The work presents CodeScan, a black-box, vulnerability-specific scanning framework for auditing poisoning and backdoor attacks in code-generation LLMs: it identifies attack targets by analyzing structural similarities across multiple generations conditioned on different clean prompts, combining iterative divergence analysis with abstract syntax tree (AST)-based normalization to abstract away surface-level variation and unify semantically equivalent code, then applies LLM-based vulnerability analysis to determine whether the extracted structures contain security vulnerabilities and flags the model as compromised when such a structure is found; evaluated against four representative attacks under both backdoor and poisoning settings across three real-world vulnerability classes, experiments o

The work presents CodeScan, a black-box, vulnerability-specific scanning framework for auditing poisoning and backdoor attacks in code-generation LLMs: it identifies attack targets by analyzing structural similarities across multiple generations conditioned on different clean prompts, combining iterative divergence analysis with abstract syntax tree (AST)-based normalization to abstract away surface-level variation and unify semantically equivalent code, then applies LLM-based vulnerability analysis to determine whether the extracted structures contain security vulnerabilities and flags the model as compromised when such a structure is found; evaluated against four representative attacks under both backdoor and poisoning settings across three real-world vulnerability classes, experiments o