NLP Research Paper Topics

NLP now underpins billions of daily interactions—from autocomplete to translation—and the research landscape is moving faster than most syllabi. We at TopicSuggestions write as academic researchers who know students need topics that balance novelty, available data, and clear evaluation so a semester project can become a solid paper. Today we will share a focused set of NLP research paper topics you can realistically scope, implement, and defend.

Research Paper Topic Ideas on NLP

We will group ideas by core methods, data and resources, applications, multilingual and low-resource work, safety and ethics, evaluation and reproducibility, and frontier trends, with quick notes on datasets, baseline models, and metrics to help you start. We keep this in a forum-style voice for easy skimming—the list begins right below.

1. Self-Reflective Optimizer-as-Data DBMS

We ask how we can expose and safely mutate the optimizer’s internal state as relational tables so that queries can introspect and steer their own future plans?
We investigate whether closed-loop self-optimization yields stable equilibria under shifting workloads and how we detect and damp oscillations?
We evaluate what access controls and declarative what-if primitives we need to prevent adversarial or accidental destabilization?

2. Pre-Query Telemetry-Driven Caching via IDE Keystroke Streams

We ask whether we can use developers’ SQL keystroke telemetry to prewarm caches before submission and how much latency we can remove with millisecond lookahead?
We investigate how we anonymize, compress, and transmit keystroke signals to the DBMS while preserving predictive value and privacy?
We evaluate how anticipatory caching interacts with multi-tenant fairness and whether mispredictions degrade neighbors?

3. Reciprocity-Constrained Access Control in Scientific Data Commons

We ask how we formalize and enforce reciprocity rules where users must contribute commensurate data or compute credits before running expensive queries?
We investigate what on-ledger accounting and audits we need to make reciprocity transparent, portable, and tamper-resistant across institutions?
We evaluate how reciprocity affects query planning, queuing, and scientific throughput under diverse community norms?

4. Causal Indexes and DO-Operator Native Querying

We ask whether we can design physical indexes that accelerate interventional do-queries over causal DAGs and how they co-exist with relational indexes?
We investigate how we extend SQL with causal constraints and how the optimizer exploits them during join ordering and pruning?
We evaluate how robust causal indexes remain under schema evolution and observational drift?

5. Perceptual-Aware Approximate Query Processing for Dashboards

We ask how we embed human perceptual thresholds into cost models so approximate queries stop when results are visually indistinguishable for the user?
We investigate which error metrics align with perception across chart types and how the DBMS learns user-specific tolerances online?
We evaluate whether perceptual AQP changes caching, pre-aggregation, and alerting policies in real-time dashboards?

6. Schema Co-Evolution Negotiation via Secure Multi-Party Computation

We ask whether organizations can privately negotiate schema changes using secure computation over workload summaries hosted inside the DBMS?
We investigate how we encode cross-organization utilities so the optimizer can trade off migration cost, compatibility, and benefit?
We evaluate what convergence guarantees and performance overheads arise when negotiations run alongside production queries?

7. Liquid Sharding from Workload Interaction Graphs

We ask how we derive ephemeral shard boundaries from live query–data interaction graphs and how frequently we should reshape partitions?
We investigate whether liquid sharding reduces cross-shard traffic without harming transactional guarantees under bursty multi-tenant loads?
We evaluate control policies that decide when to freeze or thaw shards to optimize latency, cost, and availability?

8. Counterfactual Storage for Explainable Query Answers

We ask what storage abstractions maintain minimal counterfactual tuple sets that would flip a query outcome and how we index them efficiently?
We investigate how we expose counterfactuals via SQL extensions without leaking sensitive or identifying information?
We evaluate how counterfactual maintenance interacts with updates, deletions, and time-travel queries at scale?

9. IoT Sensor Drift-Integrated Query Corrections

We ask how we model sensor drift and calibration metadata as first-class citizens so queries can be auto-corrected at execution time?
We investigate whether drift-aware operators can meet accuracy SLAs while minimizing recomputation and energy on edge clusters?
We evaluate how drift models propagate through joins, windows, and aggregates in streaming DBMSs?

10. Verified LLM-Assisted Query Rewriting with Semantic Certificates

We ask whether we can embed LLM-based rewriters into the optimizer while attaching machine-checkable certificates that prove semantic equivalence?
We investigate how we train rewriters over proof-friendly transformation spaces and how we fail safe when certificates cannot be produced?
We evaluate end-to-end speedups versus the overhead of certificate generation and verification across heterogeneous schemas and dialects?

11. Temporal commonsense induction for historical texts

— Research questions: How do commonsense assumptions shift across historical periods, and how can we induce temporally-conditioned commonsense knowledge from dated corpora?

We (TopicSuggestions) ask whether language models can represent era-specific commonsense and how to evaluate temporal plausibility.
We propose to collect dated corpora (newspapers, diaries, parliamentary records) stratified by decade/century, induce temporal embeddings and relation triples, and train time-conditioned knowledge graph completion models.
We will evaluate by creating historian-annotated temporal plausibility judgments, measuring temporal calibration (accuracy conditioned on period) and cross-period transfer (how much modern commonsense mispredicts old texts).

12. Dialectal code-mixed morphological parsing for low-resource speech-to-text

— Research questions: How can we parse morphology in spontaneous code-mixed speech where dialectal variants change morpheme boundaries, and how can sparse supervision be leveraged?

We (TopicSuggestions) ask how to design morphological parsers that handle spoken code-mixing and dialectal alternations with limited labels.
We propose to collect short code-mixed utterances with morphological annotations via targeted crowdsourcing and use multi-task learning combining phoneme-to-grapheme models, weak lexica, and contrastive pretraining across dialects.
We will evaluate on segmentation and morpheme-label accuracy, robustness to unseen dialectal morphs, and downstream ASR+NLP performance in low-resource settings.

13. Adversarial redaction-aware summarization for sensitive legal documents

— Research questions: How can summarizers produce useful outputs when input texts contain redactions or adversarial redaction patterns intended to leak information?

We (TopicSuggestions) ask how to train summarizers that preserve utility without reconstructing redacted content and that resist adversarial redaction attacks.
We propose to simulate redaction strategies (random, patterned, semantic-targeted), train summarizers with adversarial objectives that penalize inferred redactions, and incorporate uncertainty-aware generation and redaction-preserving constraints.
We will evaluate information utility, redaction-reconstruction risk (privacy leakage metrics), and adversarial robustness across legal datasets.

14. Personalized hallucination calibration for conversational assistants

— Research questions: How can we model and control hallucination propensity per user preference and per task-criticality in real time?

We (TopicSuggestions) ask how to learn per-user calibration functions that trade hallucination risk against helpfulness.
We propose to collect fine-grained feedback (accept/reject/confidence) from users, learn reinforcement-learning policies that adjust generation temperature, retrieval scope, and citation retrieval, and train meta-models that predict hallucination likelihood conditioned on user profile and query type.
We will evaluate calibration (confidence vs. correctness), user satisfaction under controlled tradeoffs, and adaptation speed for new users.

15. Pragmatic grounding via embodied physics simulation for disambiguating instructions

— Research questions: Can short physics simulations coupled with language models resolve pragmatic ambiguities (e.g., “move the cup next to the book”) more reliably than language-only reasoning?

We (TopicSuggestions) ask whether lightweight embodied simulation reduces ambiguous plan space and improves instruction following.
We propose to integrate real-time physics engines with symbolic environment representations, generate candidate action sequences from an LLM, simulate outcomes, and use simulation feedback to re-rank or refine language model plans.
We will evaluate on human-annotated ambiguity benchmarks, success rate in simulated robotic tasks, and compute/latency tradeoffs.

16. Privacy-preserving federated annotation aggregation for small-batch human-in-the-loop workflows

— Research questions: How can we aggregate noisy, small-batch annotations from distributed annotators while guaranteeing differential privacy and statistical efficiency for low-volume tasks?

We (TopicSuggestions) ask how to design aggregation algorithms that work when each labeling task receives few annotations and where annotator identities must remain private.
We propose to combine secure aggregation protocols, Bayesian label-noise models with informative priors, and local differential privacy with adaptive noise scaling calibrated to batch size.
We will evaluate label quality, privacy guarantees (epsilon), and utility on low-volume annotation tasks such as rare-event classification or specialized domain labeling.

17. Unsupervised cross-lingual rhetorical device detection across translations

— Research questions: How can we detect rhetorical devices (metaphor, anaphora, rhetorical question) in parallel translated corpora without labeled data, and how do devices shift or get lost in translation?

We (TopicSuggestions) ask how to discover device-preserving and device-altering translation patterns and to detect rhetorical function unsupervisedly.
We propose to use contrastive alignment of parallel sentences, self-supervised probing tasks (mask-and-predict spanning cues), and clustering of function-preserving phrase correspondences to induce device labels.
We will evaluate by building small bilingual test sets annotated by experts, measuring preservation rates and device transfer matrices across language pairs.

18. Multimodal semantic drift detection over social networks

— Research questions: How can we detect rapid changes in meaning for terms and memes that evolve jointly across text, images, and video in social media streams?

We (TopicSuggestions) ask how to jointly model semantic trajectories across modalities and how to surface early-warning signals for semantic drift.
We propose to build streaming multimodal embeddings that fuse text, image, and short-video features anchored to tokens/hashtags, detect distributional shifts with graph-based change-point detectors, and validate with labeled drift incidents (meme meanings, reclaimed slurs).
We will evaluate detection timeliness, precision of drift explanations (what modality or community drove it), and robustness to adversarial re-labeling.

19. Energy-efficient chain-of-reasoning compression for large-model inference

— Research questions: How can we compress or shortcut explicit chain-of-thought traces so that downstream tasks retain reasoning faithfulness while reducing inference energy and latency?

We (TopicSuggestions) ask whether compact symbolic summaries or attention-sparse checkpoints can preserve faithfulness of multi-step reasoning with less compute.
We propose to generate full chains-of-thought offline, learn compression mappings (neural sketching, symbolic distillation) that produce compact reasoning signatures, and implement selective re-expansion policies during inference only when confidence is low.
We will evaluate task accuracy, faithfulness (agreement between compressed and full chains), and energy/latency savings on arithmetic, multi-hop QA, and planning tasks.

20. Ethics-aware prompt perturbation robustness for instruction-tuned models

— Research questions: How do small, realistic prompt perturbations alter ethical behavior of instruction-tuned models, and how can we make instruction tuning robust to such perturbations?

We (TopicSuggestions) ask which classes of perturbations (lexical paraphrase, pragmatic framing, implicit context addition) flip model safety decisions and how to mitigate them.
We propose to generate systematic perturbation suites, measure ethical-policy flips, and train robustness via adversarial instruction-tuning, contrastive safety objectives, and calibration layers that detect semantic intent shifts.
We will evaluate robustness across safety benchmarks, perturbation transfer across domains, and human-aligned risk reduction.

21. Temporal Grounding of Language Models for Historical Counterfactuals

We ask: We can reliably train models to produce text that is temporally grounded to a specified historical period without introducing anachronisms? We can quantify and reduce the propensity of models to project present-day norms into past contexts? We can create metrics that evaluate historical plausibility and causal consistency in generated counterfactual narratives? We will work on this by curating aligned corpora from dated primary sources, fine-tuning temporal adapters, designing time-aware attention mechanisms, and developing human-and-archival-expert evaluation protocols for plausibility and anachronism detection.

22. Cross-Dialect Pragmatics: Intent Modeling in Code-Switched Speech

We ask: We can model pragmatic intent across code-switched utterances where dialectal switches carry discourse functions rather than lexical replacements? We can create datasets that label pragmatic shifts tied to code-switch boundaries? We can build architectures that use switch-points as signals for pragmatic inference? We will work on this by collecting naturalistic code-switched dialogue with pragmatic annotations, training models that incorporate explicit switch-point embeddings, and evaluating intent recognition and downstream dialogue policy adjustments.

23. Privacy-Preserving Extraction of Causal Relations from Clinical Notes

We ask: We can extract causal relationships from de-identified clinical text while provably preserving patient privacy? We can quantify the trade-offs between differential privacy guarantees and the fidelity of extracted causal graphs? We can design synthetic-data augmentation methods that preserve causal structures for downstream causal discovery? We will work on this by applying differentially private training to causality-aware NLP models, benchmarking causal extraction quality under varying privacy budgets, and validating on synthetic and fully de-identified clinical corpora.

24. Token-Topology Dynamics: Interpreting Continual Learning in Transformer Embedding Spaces

We ask: We can characterize how token-level geometric topology evolves during continual learning and link those dynamics to forgetting and interference? We can design topology-preserving regularizers that mitigate catastrophic forgetting without task rehearsal? We can create visualization tools that reveal emergent clusters and their drift over sequential tasks? We will work on this by instrumenting embedding spaces across training checkpoints, developing topological data analysis metrics (e.g., persistent homology) for embeddings, and evaluating regularizers that constrain topological changes.

25. Multimodal Pragmatic Repair: Aligning Gesture, Facial Expression, and Discourse Repair in Conversational Agents

We ask: We can model and generate multimodal repair behaviors (verbal repair, self-correction, gesture) that align naturally in real-time interaction? We can learn when to initiate each repair channel to maximize user comprehension and trust? We can evaluate multimodal repair effectiveness in live human-agent dialogues? We will work on this by collecting synchronized multimodal conversational corpora annotated for repair events, training joint audio-visual-text repair predictors, and running controlled user studies measuring comprehension and perceived naturalness.

26. Native-Language Influence Modeling for Low-Resource Typologically-Diverse MT

We ask: We can explicitly model native-language transfer effects to improve translation quality for speakers of low-resource, typologically-diverse languages? We can induce typological priors from limited parallel data and use them as constraints to guide MT outputs? We can evaluate improvement in preserving syntactic and pragmatic features specific to source-language communities? We will work on this by collecting speaker metadata, designing typology-conditioned encoders, and using contrastive fine-tuning with typology-aware loss functions.

27. Explainable Hallucination Attribution via Counterfactual Token Interventions

We ask: We can attribute specific hallucinated facts in model outputs to minimal token-level context elements via counterfactual interventions? We can produce human-interpretable explanations that indicate which input spans most plausibly caused a hallucination? We can use these explanations to guide targeted mitigation (e.g., input rewriting or on-the-fly retrieval augmentation)? We will work on this by designing token-intervention pipelines that ablate or replace spans, measuring hallucination sensitivity, and integrating attribution signals into inference-time safeguards.

28. Energetic and Carbon-Aware Decoding Strategies for Large Language Models

We ask: We can adapt decoding algorithms to minimize energy consumption and carbon intensity without significant loss in generation quality? We can predict, per-request, decoding parameters (beam size, length penalty) that optimize a quality-energy trade-off? We can incorporate geographically-aware dispatching of computation to reduce carbon footprint? We will work on this by instrumenting energy usage per decoding strategy, training controllers to choose decoding hyperparameters conditioned on input and desired quality, and evaluating user-perceived fidelity versus measured energy costs.

29. Social-Contextual Fairness: Measuring and Mitigating Group-Specific Misunderstanding in Interactive Systems

We ask: We can define operational metrics that capture systematic misunderstandings (misinterpretation rates) for different social groups in interactive NLP systems? We can design mitigation techniques that adapt conversational strategies to reduce group-specific misinterpretations while avoiding stereotyping? We can validate interventions in longitudinal deployments measuring retention and satisfaction across groups? We will work on this by creating scenario-based misunderstanding tests stratified by sociolinguistic variables, applying adaptive dialogue strategies, and running field studies with careful ethical oversight.

30. Language Models as World-Model Simulators for Interactive Scientific Hypothesis Generation

We ask: We can calibrate language models to simulate simplified mechanistic world-models that generate plausible, testable scientific hypotheses rather than unconstrained speculation? We can quantify the utility of model-generated hypotheses by their novelty and testability in lab settings? We can design prompting and conditioning schemes that constrain generative models to mechanistic reasoning chains and explicit assumptions? We will work on this by building small-domain world-model datasets, fine-tuning models with mechanistic constraints, and collaborating with domain scientists to evaluate hypothesis quality and downstream experimental validation.

31. Counterfactual Explanations for Generative Dialogue Models in Safety-Critical Domains

We propose methods to generate minimal counterfactual inputs that change a generative model’s unsafe response into a safe one.
We ask: Can we automatically identify and synthesize minimal counterfactual edits that flip safety-class decisions in dialogue; How do we evaluate relevance, minimality, and plausibility of counterfactuals in safety contexts; Can counterfactuals guide targeted model repair without degrading utility?
We outline how to work: We collect paired safe/unsafe dialogue instances, design constrained edit generators (token-level, span-rewrite, latent perturbations), use human-in-the-loop and automated metrics for minimality/feasibility, and run repair experiments (fine-tuning or patching) evaluating both safety and conversational quality.

32. Cross-modal Pragmatics: Inferring Speaker Intent from Text Plus Ambient Sound Transcripts

We explore pragmatic inference using transcripts augmented with ambient sound descriptions (e.g., “sirens”, “crowd”), not raw audio.
We ask: Can combining textual utterances with ambient-sound annotations improve intent prediction and implicature resolution; How robust are pragmatic inferences when ambient cues are noisy or missing; Can models learn which ambient cues are pragmatically salient?
We outline how to work: We create or annotate dialogue corpora with ambient-sound captions, train multimodal encoders over text+ambient tokens, design ablation studies for cue salience, and evaluate on intent prediction, implicature tasks, and human plausibility ratings.

33. Morphosyntactic Transfer for Endangered Languages via Synthetic Code-switching

We investigate synthetic code-switched data as a bridge to transfer morphosyntactic phenomena into models for endangered languages.
We ask: Can we synthesize realistic code-switched sentences that carry morphosyntactic cues from high-resource languages; How much synthetic code-switching improves morphological analysis and parsing versus conventional data augmentation; What constraints ensure linguistic plausibility?
We outline how to work: We generate code-switched corpora via constrained translation and morphological templates, train or adapt morphological analyzers/parsers, measure gains on gold low-resource test sets, and validate synthetic realism with field linguists.

34. Emotion Dynamics Modeling in Long-form Narratives for Psycholinguistic Profiling

We model fine-grained emotion trajectories across long narratives to profile cognitive and personality correlates of authors.
We ask: Can temporal models of emotion flow predict psycholinguistic traits better than aggregate emotion counts; Which sequence features (transitions, tempo, variance) map to specific traits; How to validate ethically and reliably?
We outline how to work: We annotate or weakly label emotion at sentence/chunk granularity, train sequence models (HMMs, transformers with temporal heads), extract dynamic features, correlate with trait labels or behavioral signals, and run stability and ethical impact analyses.

35. Evaluating Fairness of Language Models under Temporal Concept Drift

We assess how fairness metrics for LMs change as societal language and concepts drift over time.
We ask: How do demographic bias measures evolve when evaluation data reflect different time slices; Can we create drift-aware fairness benchmarks; What mitigation strategies remain robust under temporal shifts?
We outline how to work: We assemble historical and contemporary corpora with demographic signals, measure fairness (e.g., equalized odds on generation and classification) across timepoints, simulate drift intervention strategies (continual learning, reweighting), and report degradation and recovery curves.

36. Benchmarking Neural Reasoners on Counterfactual Commonsense with World-change Operators

We design a benchmark where reasoning requires applying explicit world-change operators (e.g., “if river froze”, “if gravity halved”) to counterfactual commonsense questions.
We ask: Can neural reasoners apply structured world-change operators to derive valid counterfactual conclusions; Which architectures handle operator composition and temporal chaining best; How to automatically generate diverse, evaluable instances?
We outline how to work: We formalize a small operator algebra, generate synthetic problems compositing operators on base commonsense facts, evaluate models on operator application, composition, and explanation quality, and include human validation for plausibility.

37. Unsupervised Discovery of Synthetic Dialects for Robust Speech-to-Text

We attempt to learn and synthesize plausible phonological/dialectal variants automatically to augment speech-to-text training without labeled dialect data.
We ask: Can unsupervised clustering of acoustic/textual mismatches yield synthetic dialect transformations that improve ASR robustness; How to preserve intelligibility while adding variant diversity; How to test transfer to real dialects?
We outline how to work: We analyze ASR errors across populations, induce transformation rules (phoneme/substitution patterns) via unsupervised alignment, synthesize text and/or acoustic variants, retrain or adapt ASR, and evaluate on held-out dialect recordings and perceived naturalness by speakers.

38. Privacy-preserving Fine-tuning via Causal Masking of Sensitive Features in Language Models

We propose masking causal paths from sensitive attributes to outputs during fine-tuning to reduce memorization and leakage while retaining utility.
We ask: Can we identify approximate causal feature subsets that mediate sensitive leaks and mask them effectively during adaptation; How does causal masking compare to differential privacy and selective forgetting; What are trade-offs in downstream performance?
We outline how to work: We use causal discovery proxies (influence functions, input-output attribution) to identify sensitive mediators, implement targeted masking or intervention during fine-tuning, measure membership inference and utility across tasks, and compare with baselines.

39. Explainability of Multilingual Transformers through Language-specific Attention Probes

We develop attention-based probes that are tailored to typological properties to explain transformer decisions across languages.
We ask: Can language-specific probe architectures expose functional roles of attention heads that general probes miss; How do attention-role mappings vary with typology (e.g., free word order vs. fixed); Can these insights guide multilingual pruning or transfer?
We outline how to work: We design probes incorporating typological priors (case marking, agglutination), apply them to multilingual transformers, map attention patterns to syntactic/semantic functions per language, and test pruning/transfer strategies informed by probe findings.

40. Adaptive Curriculum Learning for Code Generation with Semantic Unit Difficulty Estimation

We build adaptive curricula for code-generation models that order training examples by estimated semantic-unit difficulty (API usage, algorithmic concept).
We ask: Can we define and estimate the difficulty of semantic units in code (data-structure recursion, concurrency); Does curriculum based on these estimates speed learning and improve generalization; How to infer unit difficulty with limited labels?
We outline how to work: We decompose code into semantic units via static analysis, train difficulty predictors with small annotated sets and self-supervision (e.g., solution failure rates), implement adaptive sampling during model training, and evaluate convergence, generalization to harder tasks, and sample efficiency.

41. Contrastive Grounded Pragmatics for Code-Switched Instructions

We ask: How do we model pragmatic intent in code-switched instructional utterances when the referent is visually present; How do speakers choose language alternation to disambiguate multimodal referents; How can models learn pragmatic implicatures that depend on which language fragment carries disambiguating cues?
We will build mixed-language multimodal datasets by collecting paired code-switched instructions with images/videos; We will design contrastive learning objectives that align pragmatics signals across languages and modalities; We will evaluate with human pragmatic-choice benchmarks and ablation studies on language fragment perturbation.

42. Counterfactual Token Surgery for Hallucination Provenance

We ask: Can we attribute individual hallucinated facts in LLM outputs to specific training tokens via counterfactual token removals; How do localized token interventions change model factuality without retraining; How can provenance traces be summarized for users?
We will implement token-level influence estimators using fast approximations of influence functions and targeted pruning of gradient contributions; We will run counterfactual generation experiments on controlled corpora to link hallucinations to candidate evidence tokens; We will produce provenance visualizations and quantitative fidelity metrics comparing to gold-traceable sources.

43. Continuous-Time Sociolinguistic Drift Modeling with Transformers

We ask: How can transformer-based language models represent continuous-time sociolinguistic drift rather than discrete epochs; How do social network features and external events modulate continuous semantic shift; How can we forecast lexical and syntactic drift months or years ahead?
We will augment transformer embeddings with explicit continuous-time encodings and social-graph-conditioned attention; We will train on temporally dense corpora (social media streams, news) with event annotations; We will evaluate via retrospective prediction tasks and calibration of time-aware semantic similarity.

44. Privacy-Preserving Personalization via On-Device Latent Adapters

We ask: How can we learn compact on-device latent adapters that personalize LLM behavior without exposing private data; How do adapter updates generalize across tasks while remaining provably unlinkable to raw examples; How do we compress adapter updates for federated communication?
We will design adapter architectures that store only hashed latent statistics and train them with differential privacy constraints; We will run federated simulations comparing utility-privacy tradeoffs and test robustness to reconstruction attacks; We will optimize adapter sparsity and quantization for real device constraints.

45. Emotion-Skeptical Dialogue Systems for Stance-Resistant Advice

We ask: How can we design dialogue agents that resist misattributing emotions and provide stance-neutral advice in high-stakes conversations; How do we detect overconfident emotional inferences and correct them; How does emotion-skepticism affect user trust and outcomes?
We will create datasets of dialogues with annotated emotive uncertainty and user outcomes; We will implement modules that estimate confidence in emotion inference and default to factual clarification strategies; We will measure impact through user studies measuring perceived empathy, correctness, and trust.

46. Language-to-Sensor Prompting: Natural Language for On-Board Robot Calibration

We ask: Can robots calibrate sensor parameters (e.g., camera exposure, LIDAR filtering) from natural language prompts describing scene properties; How do we map ambiguous human descriptions to calibration actions robustly; How can models learn to query minimal clarifying questions?
We will collect paired data of human scene descriptions, calibration settings, and success metrics in simulated and real environments; We will train multimodal controllers that translate language into calibration parameter distributions with uncertainty estimates; We will evaluate on calibration efficiency and downstream task performance.

47. Fairness Auditing for Code-Mixed and Multilingual Toxicity Classifiers

We ask: How do fairness metrics behave differently for code-mixed and transliterated toxic content; Which mitigation strategies for toxicity induce disparate impacts across language mixing patterns; How can we create language-agnostic fairness constraints?
We will assemble multilingual and code-mixed toxicity corpora with demographic annotations where available; We will adapt fairness definitions to token-level and script-level mixing and test debiasing methods (reweighting, adversarial training); We will report tradeoffs in false positives/negatives across language-mixing strata.

48. Token-Efficiency Schedules for Energy-Conscious Continual Learning

We ask: Can we schedule token processing sparsity during continual learning to reduce energy while retaining plasticity; How do adaptive token drop strategies interact with catastrophic forgetting; What policies minimize compute for equivalent downstream performance?
We will implement token-sparsity schedulers that prune tokens dynamically based on novelty and gradient contribution during streaming updates; We will run continual benchmarks comparing energy consumption, memory retention, and accuracy; We will derive theoretical bounds linking token selection to forgetting rates.

49. Multimodal Commonsense via Procedural Simulation Narratives

We ask: How can procedural simulation (agent-in-environment traces) be turned into multimodal narratives that teach models commonsense about physical affordances; Which representation of procedural traces yields the best grounded commonsense reasoning; How do we test transfer to real-world question answering?
We will generate synthetic procedural traces from simulators and render multimodal narratives (text + short video snippets); We will train multimodal models on these narratives with explicit affordance labels and contrastive objectives; We will evaluate transfer to physical commonsense benchmarks and VQA with ablation on simulation fidelity.

50. Explainable Sparse LM Ensembles for Legal Drafting Assistance

We ask: How can sparse ensembles of small, expert language models produce legally accurate drafts with traceable rationales; How do we coordinate expert models to avoid contradicting statutory interpretations; How can we present ensemble reasoning traces in legally admissible formats?
We will partition legal reasoning into modular experts (statute retrieval, precedent synthesis, clause drafting) and train sparse routing to invoke experts per subtask; We will design provenance-aware aggregation that attaches citations and counterarguments to each clause; We will validate with legal professionals on drafting accuracy, coherence, and explainability.