10 papers
Incident Memory: Training-Free Operational Memory through Sequential Pattern Mining and Velocity-Stratified Retrieval
Adarsh Agrawal, Rahul Suresh Babu
Incident response is a memory problem: teams accumulate tickets, traces, postmortems, and wiki pages, but the knowledge needed for the next incident is rarely stored with its order…
Binding Drift in Multi-Step Tool-Augmented Agents
Rahul Suresh Babu, Shashank Indukuri
Tool-augmented language-model agents execute multi-step workflows over external systems, resolving an entity once and then acting on it across subsequent steps. Prior work shows th…
Entity Binding Failures in Tool-Augmented Agents
Rahul Suresh Babu, Shashank Indukuri
Tool-augmented language-model agents are often evaluated by whether they select the correct tool, produce valid API arguments, and complete the requested task. However, an agent ma…
The Gate Is Only as Honest as Its Contracts: ContractGuard for the Contract Layer of Risk-Aware Causal Gating
Laxmipriya Ganesh Iyer, Rahul Suresh Babu
Risk-Aware Causal Gating (RACG) defends tool-augmented LLM agents against indirect prompt injection by removing dangerous tools from the agent's visible action space, so that even…
GIST-CMTF: Goal-State Inference for Causal Minimal Tool Filtering in LLM Agents
Rahul Suresh Babu, Rohit Shukla
Tool-augmented LLM agents rely on runtime filtering to decide which tools should be visible at each step. Causal Minimal Tool Filtering (CMTF) reduces tool-choice confusion by expo…
ToolMenuBench: Benchmarking Tool-Menu Filtering Strategies for Reliable and Efficient LLM Agents
Rahul Suresh Babu, Laxmipriya Ganesh Iyer
Tool-augmented large language model agents increasingly operate over large tool libraries, but existing evaluations often focus on whether a model can call a tool correctly rather…
Capability Minimization as a Safety Primitive: Risk-Aware Causal Gating for Least-Privilege LLM Agents
Laxmipriya Ganesh Iyer, Rahul Suresh Babu
Modern decision systems increasingly rely on learned components whose outputs may be confident yet wrong, exposing downstream actions to costly errors. We introduce Risk-Aware Caus…
Contract2Tool: Learning Preconditions and Effects for Reliable Tool-Augmented LLM Agents
Rahul Suresh Babu, Laxmipriya Ganesh Iyer
Tool-augmented large language model agents increasingly rely on external APIs, but standard tool schemas describe how to call a tool, not when the tool is causally appropriate or w…
ToolChoiceConfusion: Causal Minimal Tool Filtering for Reliable LLM Agents
Rahul Suresh Babu, Laxmipriya Ganesh Iyer
Large language model agents increasingly rely on external tools, but larger tool menus can reduce reliability and efficiency by increasing wrong-tool calls, premature actions, and…
Self-Healing Agentic Orchestrators for Reliable Tool-Augmented Large Language Model Systems
Rahul Suresh Babu, Adarsh Agrawal
Tool-augmented large language model (LLM) agents rely on orchestration layers that coordinate planning, retrieval, tool invocation, validation, memory, and recovery. In these syste…