Aakansha Jagga
ML Researcher | GGSIPU | Multilingual Voice AI
New Delhi, India
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Aakansha Jagga is an ML researcher and student exploring multilingual voice AI, AI agents, and low-resource language systems. Her work focuses on making intelligent systems more reliable in real-world settings, especially when language, speech, and uncertainty collide. She enjoys building and evaluating practical open-source AI systems and turning research ideas into working prototypes.
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ASR Uncertainty to Agent Decisions in Multilingual Voice Systems
Multilingual voice agents often fail not because speech cannot be transcribed, but because agents act too confidently on uncertain transcripts. Accents, code-switching, noisy audio, and low-resource languages can turn small ASR errors into incorrect downstream actions.
This lightning talk explores a clarification-aware decision layer for open-source voice agents: given uncertain speech, should the agent act, confirm, or ask again? We compare confidence-threshold baselines with learned decision policies and evaluate them using task success, wrong-action rate, clarification frequency, and conversational overhead.
The talk focuses on a practical systems question: how can open voice-agent stacks use uncertainty more intelligently, reducing incorrect actions without making users repeat themselves unnecessarily?
When RAG Gets History Wrong: Preventing Anachronistic Retrieval in Multimodal AI
Retrieval-Augmented Generation is designed to make AI systems more reliable by grounding them in external evidence. But what happens when the retrieved evidence is relevant to the input yet historically wrong for the context?
This lightning talk explores anachronistic retrieval, a failure mode in multimodal RAG where a vision-language model correctly recognizes a historical artifact but grounds its interpretation in evidence from the wrong time period or cultural context, producing a plausible but historically incorrect answer.
Using historical paintings and visual archives, including Japanese collections, as a testbed, I will compare standard VLMs, generic RAG, temporal RAG, and temporal-provenance grounding to examine when retrieval introduces rather than reduces historical errors. I’ll also show how temporal metadata and source provenance can be incorporated into retrieval pipelines, alongside an evaluation framework for detecting contextually incompatible evidence.
The broader lesson extends beyond historical AI: semantic relevance alone is not enough for reliable RAG. Retrieved evidence must also be valid for the context in which it is used.
Aakansha Jagga
ML Researcher | GGSIPU | Multilingual Voice AI
New Delhi, India
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